feat: add comprehensive documentation for Python Detector Worker; include project overview, architecture, core components, and configuration details

.gitea/workflows/build.yml

@@ -0,0 +1,34 @@
+name: Build Backend Application and Docker Image
+
+on:
+  push:
+    branches:
+      - main
+  workflow_dispatch:
+
+jobs:   
+  build-docker:
+    runs-on: ubuntu-latest
+    permissions:
+      packages: write
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: git.siwatsystem.com
+          username: ${{ github.actor }}
+          password: ${{ secrets.RUNNER_TOKEN }}
+        
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          file: ./Dockerfile
+          push: true
+          tags: git.siwatsystem.com/adsist-cms/worker:latest

.gitignore

@@ -6,4 +6,7 @@ app.log
 __pycache__/
 .mptacache
 
-mptas
+mptas
+detector_worker.log
+.gitignore
+no_frame_debug.log

CLAUDE.md

@@ -0,0 +1,188 @@
+# Python Detector Worker - CLAUDE.md
+
+## Project Overview
+This is a FastAPI-based computer vision detection worker that processes video streams from RTSP/HTTP sources and runs YOLO-based machine learning pipelines for object detection and classification. The system is designed to work within a larger CMS (Content Management System) architecture.
+
+## Architecture & Technology Stack
+- **Framework**: FastAPI with WebSocket support
+- **ML/CV**: PyTorch, Ultralytics YOLO, OpenCV
+- **Containerization**: Docker (Python 3.13-bookworm base)
+- **Data Storage**: Redis integration for action handling
+- **Communication**: WebSocket-based real-time protocol
+
+## Core Components
+
+### Main Application (`app.py`)
+- **FastAPI WebSocket server** for real-time communication
+- **Multi-camera stream management** with shared stream optimization
+- **HTTP REST endpoint** for image retrieval (`/camera/{camera_id}/image`)
+- **Threading-based frame readers** for RTSP streams and HTTP snapshots
+- **Model loading and inference** using MPTA (Machine Learning Pipeline Archive) format
+- **Session management** with display identifier mapping
+- **Resource monitoring** (CPU, memory, GPU usage via psutil)
+
+### Pipeline System (`siwatsystem/pympta.py`)
+- **MPTA file handling** - ZIP archives containing model configurations
+- **Hierarchical pipeline execution** with detection → classification branching
+- **Redis action system** for image saving and message publishing
+- **Dynamic model loading** with GPU optimization
+- **Configurable trigger classes and confidence thresholds**
+
+### Testing & Debugging
+- **Protocol test script** (`test_protocol.py`) for WebSocket communication validation
+- **Pipeline webcam utility** (`pipeline_webcam.py`) for local testing with visual output
+- **RTSP streaming debug tool** (`debug/rtsp_webcam.py`) using GStreamer
+
+## Code Conventions & Patterns
+
+### Logging
+- **Structured logging** using Python's logging module
+- **File + console output** to `detector_worker.log`
+- **Debug level separation** for detailed troubleshooting
+- **Context-aware messages** with camera IDs and model information
+
+### Error Handling
+- **Graceful failure handling** with retry mechanisms (configurable max_retries)
+- **Thread-safe operations** using locks for streams and models
+- **WebSocket disconnect handling** with proper cleanup
+- **Model loading validation** with detailed error reporting
+
+### Configuration
+- **JSON configuration** (`config.json`) for runtime parameters:
+  - `poll_interval_ms`: Frame processing interval
+  - `max_streams`: Concurrent stream limit
+  - `target_fps`: Target frame rate
+  - `reconnect_interval_sec`: Stream reconnection delay
+  - `max_retries`: Maximum retry attempts (-1 for unlimited)
+
+### Threading Model
+- **Frame reader threads** for each camera stream (RTSP/HTTP)
+- **Shared stream optimization** - multiple subscriptions can reuse the same camera stream
+- **Async WebSocket handling** with concurrent task management
+- **Thread-safe data structures** with proper locking mechanisms
+
+## WebSocket Protocol
+
+### Message Types
+- **subscribe**: Start camera stream with model pipeline
+- **unsubscribe**: Stop camera stream processing
+- **requestState**: Request current worker status
+- **setSessionId**: Associate display with session identifier
+- **patchSession**: Update session data
+- **stateReport**: Periodic heartbeat with system metrics
+- **imageDetection**: Detection results with timestamp and model info
+
+### Subscription Format
+```json
+{
+  "type": "subscribe",
+  "payload": {
+    "subscriptionIdentifier": "display-001;cam-001",
+    "rtspUrl": "rtsp://...",  // OR snapshotUrl
+    "snapshotUrl": "http://...",
+    "snapshotInterval": 5000,
+    "modelUrl": "http://...model.mpta",
+    "modelId": 101,
+    "modelName": "Vehicle Detection",
+    "cropX1": 100, "cropY1": 200,
+    "cropX2": 300, "cropY2": 400
+  }
+}
+```
+
+## Model Pipeline (MPTA) Format
+
+### Structure
+- **ZIP archive** containing models and configuration
+- **pipeline.json** - Main configuration file
+- **Model files** - YOLO .pt files for detection/classification
+- **Redis configuration** - Optional for action execution
+
+### Pipeline Flow
+1. **Detection stage** - YOLO object detection with bounding boxes
+2. **Trigger evaluation** - Check if detected class matches trigger conditions
+3. **Classification stage** - Crop detected region and run classification model
+4. **Action execution** - Redis operations (image saving, message publishing)
+
+### Branch Configuration
+```json
+{
+  "modelId": "detector-v1",
+  "modelFile": "detector.pt",
+  "triggerClasses": ["car", "truck"],
+  "minConfidence": 0.5,
+  "branches": [{
+    "modelId": "classifier-v1", 
+    "modelFile": "classifier.pt",
+    "crop": true,
+    "triggerClasses": ["car"],
+    "minConfidence": 0.3,
+    "actions": [...]
+  }]
+}
+```
+
+## Stream Management
+
+### Shared Streams
+- Multiple subscriptions can share the same camera URL
+- Reference counting prevents premature stream termination
+- Automatic cleanup when last subscription ends
+
+### Frame Processing
+- **Queue-based buffering** with single frame capacity (latest frame only)
+- **Configurable polling interval** based on target FPS
+- **Automatic reconnection** with exponential backoff
+
+## Development & Testing
+
+### Local Development
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the worker
+python app.py
+
+# Test protocol compliance
+python test_protocol.py
+
+# Test pipeline with webcam
+python pipeline_webcam.py --mpta-file path/to/model.mpta --video 0
+```
+
+### Docker Deployment
+```bash
+# Build container
+docker build -t detector-worker .
+
+# Run with volume mounts for models
+docker run -p 8000:8000 -v ./models:/app/models detector-worker
+```
+
+### Testing Commands
+- **Protocol testing**: `python test_protocol.py`
+- **Pipeline validation**: `python pipeline_webcam.py --mpta-file <path> --video 0`
+- **RTSP debugging**: `python debug/rtsp_webcam.py`
+
+## Dependencies
+- **fastapi[standard]**: Web framework with WebSocket support
+- **uvicorn**: ASGI server
+- **torch, torchvision**: PyTorch for ML inference
+- **ultralytics**: YOLO implementation
+- **opencv-python**: Computer vision operations
+- **websockets**: WebSocket client/server
+- **redis**: Redis client for action execution
+
+## Security Considerations
+- Model files are loaded from trusted sources only
+- Redis connections use authentication when configured
+- WebSocket connections handle disconnects gracefully
+- Resource usage is monitored to prevent DoS
+
+## Performance Optimizations
+- GPU acceleration when CUDA is available
+- Shared camera streams reduce resource usage
+- Frame queue optimization (single latest frame)
+- Model caching across subscriptions
+- Trigger class filtering for faster inference

app.py

@@ -5,6 +5,7 @@ import time
 import queue
 import torch
 import cv2
+import numpy as np
 import base64
 import logging
 import threading
@@ -13,8 +14,9 @@ import asyncio
 import psutil
 import zipfile
 from urllib.parse import urlparse
-from fastapi import FastAPI, WebSocket
+from fastapi import FastAPI, WebSocket, HTTPException
 from fastapi.websockets import WebSocketDisconnect
+from fastapi.responses import Response
 from websockets.exceptions import ConnectionClosedError
 from ultralytics import YOLO
 
@@ -27,6 +29,12 @@ app = FastAPI()
 # "models" now holds a nested dict: { camera_id: { modelId: model_tree } }
 models: Dict[str, Dict[str, Any]] = {}
 streams: Dict[str, Dict[str, Any]] = {}
+# Store session IDs per display
+session_ids: Dict[str, int] = {}
+# Track shared camera streams by camera URL
+camera_streams: Dict[str, Dict[str, Any]] = {}
+# Map subscriptions to their camera URL
+subscription_to_camera: Dict[str, str] = {}
 
 with open("config.json", "r") as f:
     config = json.load(f)
@@ -41,145 +49,456 @@ max_retries = config.get("max_retries", 3)
 
 # Configure logging
 logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s [%(levelname)s] %(message)s",
+    level=logging.INFO,  # Set to INFO level for less verbose output
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
     handlers=[
-        logging.FileHandler("app.log"),
-        logging.StreamHandler()
+        logging.FileHandler("detector_worker.log"),  # Write logs to a file
+        logging.StreamHandler()  # Also output to console
     ]
 )
 
+# Create a logger specifically for this application
+logger = logging.getLogger("detector_worker")
+logger.setLevel(logging.DEBUG)  # Set app-specific logger to DEBUG level
+
+# Ensure all other libraries (including root) use at least INFO level
+logging.getLogger().setLevel(logging.INFO)
+
+logger.info("Starting detector worker application")
+logger.info(f"Configuration: Target FPS: {TARGET_FPS}, Max streams: {max_streams}, Max retries: {max_retries}")
+
 # Ensure the models directory exists
 os.makedirs("models", exist_ok=True)
+logger.info("Ensured models directory exists")
 
 # Constants for heartbeat and timeouts
 HEARTBEAT_INTERVAL = 2  # seconds
 WORKER_TIMEOUT_MS = 10000
+logger.debug(f"Heartbeat interval set to {HEARTBEAT_INTERVAL} seconds")
 
 # Locks for thread-safe operations
 streams_lock = threading.Lock()
 models_lock = threading.Lock()
+logger.debug("Initialized thread locks")
 
 # Add helper to download mpta ZIP file from a remote URL
 def download_mpta(url: str, dest_path: str) -> str:
     try:
+        logger.info(f"Starting download of model from {url} to {dest_path}")
         os.makedirs(os.path.dirname(dest_path), exist_ok=True)
         response = requests.get(url, stream=True)
         if response.status_code == 200:
+            file_size = int(response.headers.get('content-length', 0))
+            logger.info(f"Model file size: {file_size/1024/1024:.2f} MB")
+            downloaded = 0
             with open(dest_path, "wb") as f:
                 for chunk in response.iter_content(chunk_size=8192):
                     f.write(chunk)
-            logging.info(f"Downloaded mpta file from {url} to {dest_path}")
+                    downloaded += len(chunk)
+                    if file_size > 0 and downloaded % (file_size // 10) < 8192:  # Log approximately every 10%
+                        logger.debug(f"Download progress: {downloaded/file_size*100:.1f}%")
+            logger.info(f"Successfully downloaded mpta file from {url} to {dest_path}")
             return dest_path
         else:
-            logging.error(f"Failed to download mpta file (status code {response.status_code})")
+            logger.error(f"Failed to download mpta file (status code {response.status_code}): {response.text}")
             return None
     except Exception as e:
-        logging.error(f"Exception downloading mpta file from {url}: {e}")
+        logger.error(f"Exception downloading mpta file from {url}: {str(e)}", exc_info=True)
         return None
 
+# Add helper to fetch snapshot image from HTTP/HTTPS URL
+def fetch_snapshot(url: str):
+    try:
+        response = requests.get(url, timeout=10)
+        if response.status_code == 200:
+            # Convert response content to numpy array
+            nparr = np.frombuffer(response.content, np.uint8)
+            # Decode image
+            frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+            if frame is not None:
+                logger.debug(f"Successfully fetched snapshot from {url}, shape: {frame.shape}")
+                return frame
+            else:
+                logger.error(f"Failed to decode image from snapshot URL: {url}")
+                return None
+        else:
+            logger.error(f"Failed to fetch snapshot (status code {response.status_code}): {url}")
+            return None
+    except Exception as e:
+        logger.error(f"Exception fetching snapshot from {url}: {str(e)}")
+        return None
+
+# Helper to get crop coordinates from stream
+def get_crop_coords(stream):
+    return {
+        "cropX1": stream.get("cropX1"),
+        "cropY1": stream.get("cropY1"),
+        "cropX2": stream.get("cropX2"),
+        "cropY2": stream.get("cropY2")
+    }
+
+####################################################
+# REST API endpoint for image retrieval
+####################################################
+@app.get("/camera/{camera_id}/image")
+async def get_camera_image(camera_id: str):
+    """
+    Get the current frame from a camera as JPEG image
+    """
+    try:
+        with streams_lock:
+            if camera_id not in streams:
+                logger.warning(f"Camera ID '{camera_id}' not found in streams. Current streams: {list(streams.keys())}")
+                raise HTTPException(status_code=404, detail=f"Camera {camera_id} not found or not active")
+            
+            stream = streams[camera_id]
+            buffer = stream["buffer"]
+            logger.debug(f"Camera '{camera_id}' buffer size: {buffer.qsize()}, buffer empty: {buffer.empty()}")
+            logger.debug(f"Buffer queue contents: {getattr(buffer, 'queue', None)}")
+            
+            if buffer.empty():
+                logger.warning(f"No frame available for camera '{camera_id}'. Buffer is empty.")
+                raise HTTPException(status_code=404, detail=f"No frame available for camera {camera_id}")
+            
+            # Get the latest frame (non-blocking)
+            try:
+                frame = buffer.queue[-1]  # Get the most recent frame without removing it
+            except IndexError:
+                logger.warning(f"Buffer queue is empty for camera '{camera_id}' when trying to access last frame.")
+                raise HTTPException(status_code=404, detail=f"No frame available for camera {camera_id}")
+        # Encode frame as JPEG
+        success, buffer_img = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
+        if not success:
+            raise HTTPException(status_code=500, detail="Failed to encode image as JPEG")
+        
+        # Return image as binary response
+        return Response(content=buffer_img.tobytes(), media_type="image/jpeg")
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error retrieving image for camera {camera_id}: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
+
 ####################################################
 # Detection and frame processing functions
 ####################################################
 @app.websocket("/")
 async def detect(websocket: WebSocket):
-    logging.info("WebSocket connection accepted")
+    logger.info("WebSocket connection accepted")
     persistent_data_dict = {}
 
     async def handle_detection(camera_id, stream, frame, websocket, model_tree, persistent_data):
         try:
-            detection_result = run_pipeline(frame, model_tree)
+            # Apply crop if specified
+            cropped_frame = frame
+            if all(coord is not None for coord in [stream.get("cropX1"), stream.get("cropY1"), stream.get("cropX2"), stream.get("cropY2")]):
+                cropX1, cropY1, cropX2, cropY2 = stream["cropX1"], stream["cropY1"], stream["cropX2"], stream["cropY2"]
+                cropped_frame = frame[cropY1:cropY2, cropX1:cropX2]
+                logger.debug(f"Applied crop coordinates ({cropX1}, {cropY1}, {cropX2}, {cropY2}) to frame for camera {camera_id}")
+            
+            logger.debug(f"Processing frame for camera {camera_id} with model {stream['modelId']}")
+            start_time = time.time()
+            detection_result = run_pipeline(cropped_frame, model_tree)
+            process_time = (time.time() - start_time) * 1000
+            logger.debug(f"Detection for camera {camera_id} completed in {process_time:.2f}ms")
+            
+            # Log the raw detection result for debugging
+            logger.debug(f"Raw detection result for camera {camera_id}:\n{json.dumps(detection_result, indent=2, default=str)}")
+            
+            # Direct class result (no detections/classifications structure)
+            if detection_result and isinstance(detection_result, dict) and "class" in detection_result and "confidence" in detection_result:
+                highest_confidence_detection = {
+                    "class": detection_result.get("class", "none"),
+                    "confidence": detection_result.get("confidence", 1.0),
+                    "box": [0, 0, 0, 0]  # Empty bounding box for classifications
+                }
+            # Handle case when no detections found or result is empty
+            elif not detection_result or not detection_result.get("detections"):
+                # Check if we have classification results
+                if detection_result and detection_result.get("classifications"):
+                    # Get the highest confidence classification
+                    classifications = detection_result.get("classifications", [])
+                    highest_confidence_class = max(classifications, key=lambda x: x.get("confidence", 0)) if classifications else None
+                    
+                    if highest_confidence_class:
+                        highest_confidence_detection = {
+                            "class": highest_confidence_class.get("class", "none"),
+                            "confidence": highest_confidence_class.get("confidence", 1.0),
+                            "box": [0, 0, 0, 0]  # Empty bounding box for classifications
+                        }
+                    else:
+                        highest_confidence_detection = {
+                            "class": "none",
+                            "confidence": 1.0,
+                            "box": [0, 0, 0, 0]
+                        }
+                else:
+                    highest_confidence_detection = {
+                        "class": "none",
+                        "confidence": 1.0,
+                        "box": [0, 0, 0, 0]
+                    }
+            else:
+                # Find detection with highest confidence
+                detections = detection_result.get("detections", [])
+                highest_confidence_detection = max(detections, key=lambda x: x.get("confidence", 0)) if detections else {
+                    "class": "none",
+                    "confidence": 1.0,
+                    "box": [0, 0, 0, 0]
+                }
+            
+            # Convert detection format to match protocol - flatten detection attributes
+            detection_dict = {}
+            
+            # Handle different detection result formats
+            if isinstance(highest_confidence_detection, dict):
+                # Copy all fields from the detection result
+                for key, value in highest_confidence_detection.items():
+                    if key not in ["box", "id"]:  # Skip internal fields
+                        detection_dict[key] = value
+            
+            # Extract display identifier for session ID lookup
+            subscription_parts = stream["subscriptionIdentifier"].split(';')
+            display_identifier = subscription_parts[0] if subscription_parts else None
+            session_id = session_ids.get(display_identifier) if display_identifier else None
+            
             detection_data = {
                 "type": "imageDetection",
-                "cameraIdentifier": camera_id,
-                "timestamp": time.time(),
+                "subscriptionIdentifier": stream["subscriptionIdentifier"],
+                "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S.%fZ", time.gmtime()),
                 "data": {
-                    "detection": detection_result if detection_result else None,
+                    "detection": detection_dict,
                     "modelId": stream["modelId"],
                     "modelName": stream["modelName"]
                 }
             }
-            logging.debug(f"Sending detection data for camera {camera_id}: {detection_data}")
+            
+            # Add session ID if available
+            if session_id is not None:
+                detection_data["sessionId"] = session_id
+            
+            if highest_confidence_detection["class"] != "none":
+                logger.info(f"Camera {camera_id}: Detected {highest_confidence_detection['class']} with confidence {highest_confidence_detection['confidence']:.2f} using model {stream['modelName']}")
+                
+                # Log session ID if available
+                subscription_parts = stream["subscriptionIdentifier"].split(';')
+                display_identifier = subscription_parts[0] if subscription_parts else None
+                session_id = session_ids.get(display_identifier) if display_identifier else None
+                if session_id:
+                    logger.debug(f"Detection associated with session ID: {session_id}")
+            
             await websocket.send_json(detection_data)
+            logger.debug(f"Sent detection data to client for camera {camera_id}")
             return persistent_data
         except Exception as e:
-            logging.error(f"Error in handle_detection for camera {camera_id}: {e}")
+            logger.error(f"Error in handle_detection for camera {camera_id}: {str(e)}", exc_info=True)
             return persistent_data
 
     def frame_reader(camera_id, cap, buffer, stop_event):
         retries = 0
+        logger.info(f"Starting frame reader thread for camera {camera_id}")
+        frame_count = 0
+        last_log_time = time.time()
+        
         try:
+            # Log initial camera status and properties
+            if cap.isOpened():
+                width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                fps = cap.get(cv2.CAP_PROP_FPS)
+                logger.info(f"Camera {camera_id} opened successfully with resolution {width}x{height}, FPS: {fps}")
+            else:
+                logger.error(f"Camera {camera_id} failed to open initially")
+            
             while not stop_event.is_set():
                 try:
+                    if not cap.isOpened():
+                        logger.error(f"Camera {camera_id} is not open before trying to read")
+                        # Attempt to reopen
+                        cap = cv2.VideoCapture(streams[camera_id]["rtsp_url"])
+                        time.sleep(reconnect_interval)
+                        continue
+                    
+                    logger.debug(f"Attempting to read frame from camera {camera_id}")
                     ret, frame = cap.read()
+                    
                     if not ret:
-                        logging.warning(f"Connection lost for camera: {camera_id}, retry {retries+1}/{max_retries}")
+                        logger.warning(f"Connection lost for camera: {camera_id}, retry {retries+1}/{max_retries}")
                         cap.release()
                         time.sleep(reconnect_interval)
                         retries += 1
                         if retries > max_retries and max_retries != -1:
-                            logging.error(f"Max retries reached for camera: {camera_id}")
+                            logger.error(f"Max retries reached for camera: {camera_id}, stopping frame reader")
                             break
                         # Re-open
+                        logger.info(f"Attempting to reopen RTSP stream for camera: {camera_id}")
                         cap = cv2.VideoCapture(streams[camera_id]["rtsp_url"])
                         if not cap.isOpened():
-                            logging.error(f"Failed to reopen RTSP stream for camera: {camera_id}")
+                            logger.error(f"Failed to reopen RTSP stream for camera: {camera_id}")
                             continue
+                        logger.info(f"Successfully reopened RTSP stream for camera: {camera_id}")
                         continue
+                    
+                    # Successfully read a frame
+                    frame_count += 1
+                    current_time = time.time()
+                    # Log frame stats every 5 seconds
+                    if current_time - last_log_time > 5:
+                        logger.info(f"Camera {camera_id}: Read {frame_count} frames in the last {current_time - last_log_time:.1f} seconds")
+                        frame_count = 0
+                        last_log_time = current_time
+                    
+                    logger.debug(f"Successfully read frame from camera {camera_id}, shape: {frame.shape}")
                     retries = 0
+                    
                     # Overwrite old frame if buffer is full
                     if not buffer.empty():
                         try:
                             buffer.get_nowait()
+                            logger.debug(f"[frame_reader] Removed old frame from buffer for camera {camera_id}")
                         except queue.Empty:
                             pass
                     buffer.put(frame)
+                    logger.debug(f"[frame_reader] Added new frame to buffer for camera {camera_id}. Buffer size: {buffer.qsize()}")
+                    
+                    # Short sleep to avoid CPU overuse
+                    time.sleep(0.01)
+                    
                 except cv2.error as e:
-                    logging.error(f"OpenCV error for camera {camera_id}: {e}")
+                    logger.error(f"OpenCV error for camera {camera_id}: {e}", exc_info=True)
                     cap.release()
                     time.sleep(reconnect_interval)
                     retries += 1
                     if retries > max_retries and max_retries != -1:
-                        logging.error(f"Max retries reached after OpenCV error for camera {camera_id}")
+                        logger.error(f"Max retries reached after OpenCV error for camera {camera_id}")
                         break
+                    logger.info(f"Attempting to reopen RTSP stream after OpenCV error for camera: {camera_id}")
                     cap = cv2.VideoCapture(streams[camera_id]["rtsp_url"])
                     if not cap.isOpened():
-                        logging.error(f"Failed to reopen RTSP stream for camera {camera_id} after OpenCV error")
+                        logger.error(f"Failed to reopen RTSP stream for camera {camera_id} after OpenCV error")
                         continue
+                    logger.info(f"Successfully reopened RTSP stream after OpenCV error for camera: {camera_id}")
                 except Exception as e:
-                    logging.error(f"Unexpected error for camera {camera_id}: {e}")
+                    logger.error(f"Unexpected error for camera {camera_id}: {str(e)}", exc_info=True)
                     cap.release()
                     break
         except Exception as e:
-            logging.error(f"Error in frame_reader thread for camera {camera_id}: {e}")
+            logger.error(f"Error in frame_reader thread for camera {camera_id}: {str(e)}", exc_info=True)
+        finally:
+            logger.info(f"Frame reader thread for camera {camera_id} is exiting")
+            if cap and cap.isOpened():
+                cap.release()
+
+    def snapshot_reader(camera_id, snapshot_url, snapshot_interval, buffer, stop_event):
+        """Frame reader that fetches snapshots from HTTP/HTTPS URL at specified intervals"""
+        retries = 0
+        logger.info(f"Starting snapshot reader thread for camera {camera_id} from {snapshot_url}")
+        frame_count = 0
+        last_log_time = time.time()
+        
+        try:
+            interval_seconds = snapshot_interval / 1000.0  # Convert milliseconds to seconds
+            logger.info(f"Snapshot interval for camera {camera_id}: {interval_seconds}s")
+            
+            while not stop_event.is_set():
+                try:
+                    start_time = time.time()
+                    frame = fetch_snapshot(snapshot_url)
+                    
+                    if frame is None:
+                        logger.warning(f"Failed to fetch snapshot for camera: {camera_id}, retry {retries+1}/{max_retries}")
+                        retries += 1
+                        if retries > max_retries and max_retries != -1:
+                            logger.error(f"Max retries reached for snapshot camera: {camera_id}, stopping reader")
+                            break
+                        time.sleep(min(interval_seconds, reconnect_interval))
+                        continue
+                    
+                    # Successfully fetched a frame
+                    frame_count += 1
+                    current_time = time.time()
+                    # Log frame stats every 5 seconds
+                    if current_time - last_log_time > 5:
+                        logger.info(f"Camera {camera_id}: Fetched {frame_count} snapshots in the last {current_time - last_log_time:.1f} seconds")
+                        frame_count = 0
+                        last_log_time = current_time
+                    
+                    logger.debug(f"Successfully fetched snapshot from camera {camera_id}, shape: {frame.shape}")
+                    retries = 0
+                    
+                    # Overwrite old frame if buffer is full
+                    if not buffer.empty():
+                        try:
+                            buffer.get_nowait()
+                            logger.debug(f"[snapshot_reader] Removed old snapshot from buffer for camera {camera_id}")
+                        except queue.Empty:
+                            pass
+                    buffer.put(frame)
+                    logger.debug(f"[snapshot_reader] Added new snapshot to buffer for camera {camera_id}. Buffer size: {buffer.qsize()}")
+                    
+                    # Wait for the specified interval
+                    elapsed = time.time() - start_time
+                    sleep_time = max(interval_seconds - elapsed, 0)
+                    if sleep_time > 0:
+                        time.sleep(sleep_time)
+                
+                except Exception as e:
+                    logger.error(f"Unexpected error fetching snapshot for camera {camera_id}: {str(e)}", exc_info=True)
+                    retries += 1
+                    if retries > max_retries and max_retries != -1:
+                        logger.error(f"Max retries reached after error for snapshot camera {camera_id}")
+                        break
+                    time.sleep(min(interval_seconds, reconnect_interval))
+        except Exception as e:
+            logger.error(f"Error in snapshot_reader thread for camera {camera_id}: {str(e)}", exc_info=True)
+        finally:
+            logger.info(f"Snapshot reader thread for camera {camera_id} is exiting")
 
     async def process_streams():
-        logging.info("Started processing streams")
+        logger.info("Started processing streams")
         try:
             while True:
                 start_time = time.time()
                 with streams_lock:
                     current_streams = list(streams.items())
+                    if current_streams:
+                        logger.debug(f"Processing {len(current_streams)} active streams")
+                    else:
+                        logger.debug("No active streams to process")
+                
                 for camera_id, stream in current_streams:
                     buffer = stream["buffer"]
-                    if not buffer.empty():
-                        frame = buffer.get()
-                        with models_lock:
-                            model_tree = models.get(camera_id, {}).get(stream["modelId"])
-                        key = (camera_id, stream["modelId"])
-                        persistent_data = persistent_data_dict.get(key, {})
-                        updated_persistent_data = await handle_detection(
-                            camera_id, stream, frame, websocket, model_tree, persistent_data
-                        )
-                        persistent_data_dict[key] = updated_persistent_data
+                    if buffer.empty():
+                        logger.debug(f"Frame buffer is empty for camera {camera_id}")
+                        continue
+                    
+                    logger.debug(f"Got frame from buffer for camera {camera_id}")
+                    frame = buffer.get()
+                    
+                    with models_lock:
+                        model_tree = models.get(camera_id, {}).get(stream["modelId"])
+                        if not model_tree:
+                            logger.warning(f"Model not found for camera {camera_id}, modelId {stream['modelId']}")
+                            continue
+                        logger.debug(f"Found model tree for camera {camera_id}, modelId {stream['modelId']}")
+                    
+                    key = (camera_id, stream["modelId"])
+                    persistent_data = persistent_data_dict.get(key, {})
+                    logger.debug(f"Starting detection for camera {camera_id} with modelId {stream['modelId']}")
+                    updated_persistent_data = await handle_detection(
+                        camera_id, stream, frame, websocket, model_tree, persistent_data
+                    )
+                    persistent_data_dict[key] = updated_persistent_data
+                
                 elapsed_time = (time.time() - start_time) * 1000  # ms
                 sleep_time = max(poll_interval - elapsed_time, 0)
-                logging.debug(f"Elapsed time: {elapsed_time}ms, sleeping for: {sleep_time}ms")
+                logger.debug(f"Frame processing cycle: {elapsed_time:.2f}ms, sleeping for: {sleep_time:.2f}ms")
                 await asyncio.sleep(sleep_time / 1000.0)
         except asyncio.CancelledError:
-            logging.info("Stream processing task cancelled")
+            logger.info("Stream processing task cancelled")
         except Exception as e:
-            logging.error(f"Error in process_streams: {e}")
+            logger.error(f"Error in process_streams: {str(e)}", exc_info=True)
 
     async def send_heartbeat():
         while True:
@@ -187,18 +506,19 @@ async def detect(websocket: WebSocket):
                 cpu_usage = psutil.cpu_percent()
                 memory_usage = psutil.virtual_memory().percent
                 if torch.cuda.is_available():
-                    gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2)  # MB
-                    gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)  # MB
+                    gpu_usage = torch.cuda.utilization() if hasattr(torch.cuda, 'utilization') else None
+                    gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)
                 else:
                     gpu_usage = None
                     gpu_memory_usage = None
 
                 camera_connections = [
                     {
-                        "cameraIdentifier": camera_id,
+                        "subscriptionIdentifier": stream["subscriptionIdentifier"],
                         "modelId": stream["modelId"],
                         "modelName": stream["modelName"],
-                        "online": True
+                        "online": True,
+                        **{k: v for k, v in get_crop_coords(stream).items() if v is not None}
                     }
                     for camera_id, stream in streams.items()
                 ]
@@ -212,104 +532,225 @@ async def detect(websocket: WebSocket):
                     "cameraConnections": camera_connections
                 }
                 await websocket.send_text(json.dumps(state_report))
-                logging.debug("Sent stateReport as heartbeat")
+                logger.debug(f"Sent stateReport as heartbeat: CPU {cpu_usage:.1f}%, Memory {memory_usage:.1f}%, {len(camera_connections)} active cameras")
                 await asyncio.sleep(HEARTBEAT_INTERVAL)
             except Exception as e:
-                logging.error(f"Error sending stateReport heartbeat: {e}")
+                logger.error(f"Error sending stateReport heartbeat: {e}")
                 break
 
     async def on_message():
         while True:
             try:
                 msg = await websocket.receive_text()
-                logging.debug(f"Received message: {msg}")
+                logger.debug(f"Received message: {msg}")
                 data = json.loads(msg)
                 msg_type = data.get("type")
 
                 if msg_type == "subscribe":
                     payload = data.get("payload", {})
-                    camera_id = payload.get("cameraIdentifier")
+                    subscriptionIdentifier = payload.get("subscriptionIdentifier")
                     rtsp_url = payload.get("rtspUrl")
-                    model_url = payload.get("modelUrl")  # may be remote or local
+                    snapshot_url = payload.get("snapshotUrl")
+                    snapshot_interval = payload.get("snapshotInterval")
+                    model_url = payload.get("modelUrl")
                     modelId = payload.get("modelId")
                     modelName = payload.get("modelName")
+                    cropX1 = payload.get("cropX1")
+                    cropY1 = payload.get("cropY1")
+                    cropX2 = payload.get("cropX2")
+                    cropY2 = payload.get("cropY2")
+
+                    # Extract camera_id from subscriptionIdentifier (format: displayIdentifier;cameraIdentifier)
+                    parts = subscriptionIdentifier.split(';')
+                    if len(parts) != 2:
+                        logger.error(f"Invalid subscriptionIdentifier format: {subscriptionIdentifier}")
+                        continue
+                    
+                    display_identifier, camera_identifier = parts
+                    camera_id = subscriptionIdentifier  # Use full subscriptionIdentifier as camera_id for mapping
 
                     if model_url:
                         with models_lock:
-                            if camera_id not in models:
-                                models[camera_id] = {}
-                            if modelId not in models[camera_id]:
-                                logging.info(f"Loading model from {model_url}")
-                                extraction_dir = os.path.join("models", camera_id, str(modelId))
+                            if (camera_id not in models) or (modelId not in models[camera_id]):
+                                logger.info(f"Loading model from {model_url} for camera {camera_id}, modelId {modelId}")
+                                extraction_dir = os.path.join("models", camera_identifier, str(modelId))
                                 os.makedirs(extraction_dir, exist_ok=True)
                                 # If model_url is remote, download it first.
                                 parsed = urlparse(model_url)
                                 if parsed.scheme in ("http", "https"):
-                                    local_mpta = os.path.join(extraction_dir, os.path.basename(parsed.path))
+                                    logger.info(f"Downloading remote .mpta file from {model_url}")
+                                    filename = os.path.basename(parsed.path) or f"model_{modelId}.mpta"
+                                    local_mpta = os.path.join(extraction_dir, filename)
+                                    logger.debug(f"Download destination: {local_mpta}")
                                     local_path = download_mpta(model_url, local_mpta)
                                     if not local_path:
-                                        logging.error("Failed to download the remote mpta file.")
+                                        logger.error(f"Failed to download the remote .mpta file from {model_url}")
+                                        error_response = {
+                                            "type": "error",
+                                            "subscriptionIdentifier": subscriptionIdentifier,
+                                            "error": f"Failed to download model from {model_url}"
+                                        }
+                                        await websocket.send_json(error_response)
                                         continue
                                     model_tree = load_pipeline_from_zip(local_path, extraction_dir)
                                 else:
+                                    logger.info(f"Loading local .mpta file from {model_url}")
+                                    # Check if file exists before attempting to load
+                                    if not os.path.exists(model_url):
+                                        logger.error(f"Local .mpta file not found: {model_url}")
+                                        logger.debug(f"Current working directory: {os.getcwd()}")
+                                        error_response = {
+                                            "type": "error",
+                                            "subscriptionIdentifier": subscriptionIdentifier,
+                                            "error": f"Model file not found: {model_url}"
+                                        }
+                                        await websocket.send_json(error_response)
+                                        continue
                                     model_tree = load_pipeline_from_zip(model_url, extraction_dir)
                                 if model_tree is None:
-                                    logging.error("Failed to load model from mpta file.")
+                                    logger.error(f"Failed to load model {modelId} from .mpta file for camera {camera_id}")
+                                    error_response = {
+                                        "type": "error",
+                                        "subscriptionIdentifier": subscriptionIdentifier,
+                                        "error": f"Failed to load model {modelId}"
+                                    }
+                                    await websocket.send_json(error_response)
                                     continue
+                                if camera_id not in models:
+                                    models[camera_id] = {}
                                 models[camera_id][modelId] = model_tree
-                                logging.info(f"Loaded model {modelId} for camera {camera_id}")
-
-                    if camera_id and rtsp_url:
+                                logger.info(f"Successfully loaded model {modelId} for camera {camera_id}")
+                                logger.debug(f"Model extraction directory: {extraction_dir}")
+                    if camera_id and (rtsp_url or snapshot_url):
                         with streams_lock:
+                            # Determine camera URL for shared stream management
+                            camera_url = snapshot_url if snapshot_url else rtsp_url
+                            
                             if camera_id not in streams and len(streams) < max_streams:
-                                cap = cv2.VideoCapture(rtsp_url)
-                                if not cap.isOpened():
-                                    logging.error(f"Failed to open RTSP stream for camera {camera_id}")
-                                    continue
-                                buffer = queue.Queue(maxsize=1)
-                                stop_event = threading.Event()
-                                thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
-                                thread.daemon = True
-                                thread.start()
-                                streams[camera_id] = {
-                                    "cap": cap,
+                                # Check if we already have a stream for this camera URL
+                                shared_stream = camera_streams.get(camera_url)
+                                
+                                if shared_stream:
+                                    # Reuse existing stream
+                                    logger.info(f"Reusing existing stream for camera URL: {camera_url}")
+                                    buffer = shared_stream["buffer"]
+                                    stop_event = shared_stream["stop_event"]
+                                    thread = shared_stream["thread"]
+                                    mode = shared_stream["mode"]
+                                    
+                                    # Increment reference count
+                                    shared_stream["ref_count"] = shared_stream.get("ref_count", 0) + 1
+                                else:
+                                    # Create new stream
+                                    buffer = queue.Queue(maxsize=1)
+                                    stop_event = threading.Event()
+                                    
+                                    if snapshot_url and snapshot_interval:
+                                        logger.info(f"Creating new snapshot stream for camera {camera_id}: {snapshot_url}")
+                                        thread = threading.Thread(target=snapshot_reader, args=(camera_identifier, snapshot_url, snapshot_interval, buffer, stop_event))
+                                        thread.daemon = True
+                                        thread.start()
+                                        mode = "snapshot"
+                                        
+                                        # Store shared stream info
+                                        shared_stream = {
+                                            "buffer": buffer,
+                                            "thread": thread,
+                                            "stop_event": stop_event,
+                                            "mode": mode,
+                                            "url": snapshot_url,
+                                            "snapshot_interval": snapshot_interval,
+                                            "ref_count": 1
+                                        }
+                                        camera_streams[camera_url] = shared_stream
+                                        
+                                    elif rtsp_url:
+                                        logger.info(f"Creating new RTSP stream for camera {camera_id}: {rtsp_url}")
+                                        cap = cv2.VideoCapture(rtsp_url)
+                                        if not cap.isOpened():
+                                            logger.error(f"Failed to open RTSP stream for camera {camera_id}")
+                                            continue
+                                        thread = threading.Thread(target=frame_reader, args=(camera_identifier, cap, buffer, stop_event))
+                                        thread.daemon = True
+                                        thread.start()
+                                        mode = "rtsp"
+                                        
+                                        # Store shared stream info
+                                        shared_stream = {
+                                            "buffer": buffer,
+                                            "thread": thread,
+                                            "stop_event": stop_event,
+                                            "mode": mode,
+                                            "url": rtsp_url,
+                                            "cap": cap,
+                                            "ref_count": 1
+                                        }
+                                        camera_streams[camera_url] = shared_stream
+                                    else:
+                                        logger.error(f"No valid URL provided for camera {camera_id}")
+                                        continue
+                                
+                                # Create stream info for this subscription
+                                stream_info = {
                                     "buffer": buffer,
                                     "thread": thread,
-                                    "rtsp_url": rtsp_url,
                                     "stop_event": stop_event,
                                     "modelId": modelId,
-                                    "modelName": modelName
+                                    "modelName": modelName,
+                                    "subscriptionIdentifier": subscriptionIdentifier,
+                                    "cropX1": cropX1,
+                                    "cropY1": cropY1,
+                                    "cropX2": cropX2,
+                                    "cropY2": cropY2,
+                                    "mode": mode,
+                                    "camera_url": camera_url
                                 }
-                                logging.info(f"Subscribed to camera {camera_id} with modelId {modelId}, modelName {modelName}, URL {rtsp_url}")
+                                
+                                if mode == "snapshot":
+                                    stream_info["snapshot_url"] = snapshot_url
+                                    stream_info["snapshot_interval"] = snapshot_interval
+                                elif mode == "rtsp":
+                                    stream_info["rtsp_url"] = rtsp_url
+                                    stream_info["cap"] = shared_stream["cap"]
+                                
+                                streams[camera_id] = stream_info
+                                subscription_to_camera[camera_id] = camera_url
+                                
                             elif camera_id and camera_id in streams:
-                                # If already subscribed, unsubscribe
-                                stream = streams.pop(camera_id)
-                                stream["cap"].release()
-                                logging.info(f"Unsubscribed from camera {camera_id}")
-                                with models_lock:
-                                    if camera_id in models and modelId in models[camera_id]:
-                                        del models[camera_id][modelId]
-                                        if not models[camera_id]:
-                                            del models[camera_id]
+                                # If already subscribed, unsubscribe first
+                                logger.info(f"Resubscribing to camera {camera_id}")
+                                # Note: Keep models in memory for reuse across subscriptions
                 elif msg_type == "unsubscribe":
                     payload = data.get("payload", {})
-                    camera_id = payload.get("cameraIdentifier")
-                    logging.debug(f"Unsubscribing from camera {camera_id}")
+                    subscriptionIdentifier = payload.get("subscriptionIdentifier")
+                    camera_id = subscriptionIdentifier
                     with streams_lock:
                         if camera_id and camera_id in streams:
                             stream = streams.pop(camera_id)
-                            stream["stop_event"].set()
-                            stream["thread"].join()
-                            stream["cap"].release()
-                            logging.info(f"Unsubscribed from camera {camera_id}")
-                            with models_lock:
-                                if camera_id in models:
-                                    del models[camera_id]
+                            camera_url = subscription_to_camera.pop(camera_id, None)
+                            
+                            if camera_url and camera_url in camera_streams:
+                                shared_stream = camera_streams[camera_url]
+                                shared_stream["ref_count"] -= 1
+                                
+                                # If no more references, stop the shared stream
+                                if shared_stream["ref_count"] <= 0:
+                                    logger.info(f"Stopping shared stream for camera URL: {camera_url}")
+                                    shared_stream["stop_event"].set()
+                                    shared_stream["thread"].join()
+                                    if "cap" in shared_stream:
+                                        shared_stream["cap"].release()
+                                    del camera_streams[camera_url]
+                                else:
+                                    logger.info(f"Shared stream for {camera_url} still has {shared_stream['ref_count']} references")
+                            
+                            logger.info(f"Unsubscribed from camera {camera_id}")
+                            # Note: Keep models in memory for potential reuse
                 elif msg_type == "requestState":
                     cpu_usage = psutil.cpu_percent()
                     memory_usage = psutil.virtual_memory().percent
                     if torch.cuda.is_available():
-                        gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2)
+                        gpu_usage = torch.cuda.utilization() if hasattr(torch.cuda, 'utilization') else None
                         gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)
                     else:
                         gpu_usage = None
@@ -317,10 +758,11 @@ async def detect(websocket: WebSocket):
 
                     camera_connections = [
                         {
-                            "cameraIdentifier": camera_id,
+                            "subscriptionIdentifier": stream["subscriptionIdentifier"],
                             "modelId": stream["modelId"],
                             "modelName": stream["modelName"],
-                            "online": True
+                            "online": True,
+                            **{k: v for k, v in get_crop_coords(stream).items() if v is not None}
                         }
                         for camera_id, stream in streams.items()
                     ]
@@ -334,17 +776,47 @@ async def detect(websocket: WebSocket):
                         "cameraConnections": camera_connections
                     }
                     await websocket.send_text(json.dumps(state_report))
+                
+                elif msg_type == "setSessionId":
+                    payload = data.get("payload", {})
+                    display_identifier = payload.get("displayIdentifier")
+                    session_id = payload.get("sessionId")
+                    
+                    if display_identifier:
+                        # Store session ID for this display
+                        if session_id is None:
+                            session_ids.pop(display_identifier, None)
+                            logger.info(f"Cleared session ID for display {display_identifier}")
+                        else:
+                            session_ids[display_identifier] = session_id
+                            logger.info(f"Set session ID {session_id} for display {display_identifier}")
+                
+                elif msg_type == "patchSession":
+                    session_id = data.get("sessionId")
+                    patch_data = data.get("data", {})
+                    
+                    # For now, just acknowledge the patch - actual implementation depends on backend requirements
+                    response = {
+                        "type": "patchSessionResult",
+                        "payload": {
+                            "sessionId": session_id,
+                            "success": True,
+                            "message": "Session patch acknowledged"
+                        }
+                    }
+                    await websocket.send_json(response)
+                    logger.info(f"Acknowledged patch for session {session_id}")
+                
                 else:
-                    logging.error(f"Unknown message type: {msg_type}")
+                    logger.error(f"Unknown message type: {msg_type}")
             except json.JSONDecodeError:
-                logging.error("Received invalid JSON message")
+                logger.error("Received invalid JSON message")
             except (WebSocketDisconnect, ConnectionClosedError) as e:
-                logging.warning(f"WebSocket disconnected: {e}")
+                logger.warning(f"WebSocket disconnected: {e}")
                 break
             except Exception as e:
-                logging.error(f"Error handling message: {e}")
+                logger.error(f"Error handling message: {e}")
                 break
-
     try:
         await websocket.accept()
         stream_task = asyncio.create_task(process_streams())
@@ -352,22 +824,28 @@ async def detect(websocket: WebSocket):
         message_task = asyncio.create_task(on_message())
         await asyncio.gather(heartbeat_task, message_task)
     except Exception as e:
-        logging.error(f"Error in detect websocket: {e}")
+        logger.error(f"Error in detect websocket: {e}")
     finally:
         stream_task.cancel()
         await stream_task
         with streams_lock:
-            for camera_id, stream in streams.items():
-                stream["stop_event"].set()
-                stream["thread"].join()
-                stream["cap"].release()
-                while not stream["buffer"].empty():
+            # Clean up shared camera streams
+            for camera_url, shared_stream in camera_streams.items():
+                shared_stream["stop_event"].set()
+                shared_stream["thread"].join()
+                if "cap" in shared_stream:
+                    shared_stream["cap"].release()
+                while not shared_stream["buffer"].empty():
                     try:
-                        stream["buffer"].get_nowait()
+                        shared_stream["buffer"].get_nowait()
                     except queue.Empty:
                         pass
-                logging.info(f"Released camera {camera_id} and cleaned up resources")
+                logger.info(f"Released shared camera stream for {camera_url}")
+            
             streams.clear()
+            camera_streams.clear()
+            subscription_to_camera.clear()
         with models_lock:
             models.clear()
-        logging.info("WebSocket connection closed")
+        session_ids.clear()
+        logger.info("WebSocket connection closed")

app_single.py

@@ -1,366 +0,0 @@
-from typing import List
-from fastapi import FastAPI, WebSocket
-from fastapi.websockets import WebSocketDisconnect
-from websockets.exceptions import ConnectionClosedError
-from ultralytics import YOLO
-import torch
-import cv2
-import base64
-import numpy as np
-import json
-import logging
-import threading
-import queue
-import os
-import requests
-from urllib.parse import urlparse
-import asyncio
-import psutil
-
-app = FastAPI()
-
-models = {}
-
-with open("config.json", "r") as f:
-    config = json.load(f)
-
-poll_interval = config.get("poll_interval_ms", 100)
-reconnect_interval = config.get("reconnect_interval_sec", 5) 
-TARGET_FPS = config.get("target_fps", 10)
-poll_interval = 1000 / TARGET_FPS
-logging.info(f"Poll interval: {poll_interval}ms")
-max_streams = config.get("max_streams", 5)
-max_retries = config.get("max_retries", 3)
-
-# Configure logging
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s [%(levelname)s] %(message)s",
-    handlers=[
-        logging.FileHandler("app.log"),
-        logging.StreamHandler()
-    ]
-)
-
-# Ensure the models directory exists
-os.makedirs("models", exist_ok=True)
-
-# Add constants for heartbeat
-HEARTBEAT_INTERVAL = 2  # seconds
-WORKER_TIMEOUT_MS = 10000
-
-# Add a lock for thread-safe operations on shared resources
-streams_lock = threading.Lock()
-models_lock = threading.Lock()
-
-@app.websocket("/")
-async def detect(websocket: WebSocket):
-    import asyncio
-    import time
-
-    logging.info("WebSocket connection accepted")
-
-    streams = {}
-
-    # This function is user-modifiable
-    # Save data you want to persist across frames in the persistent_data dictionary
-    async def handle_detection(camera_id, stream, frame, websocket, model: YOLO, persistent_data):
-        try:
-            highest_conf_box = None
-            max_conf = -1
-            
-            for r in model.track(frame, stream=False, persist=True):
-                for box in r.boxes:
-                    box_cpu = box.cpu()
-                    conf = float(box_cpu.conf[0])
-                    if conf > max_conf and hasattr(box, "id") and box.id is not None:
-                        max_conf = conf
-                        highest_conf_box = {
-                            "class": model.names[int(box_cpu.cls[0])],
-                            "confidence": conf,
-                            "id": box.id.item(),
-                        }
-            
-            # Broadcast to all subscribers of this URL
-            detection_data = {
-                "type": "imageDetection",
-                "cameraIdentifier": camera_id,
-                "timestamp": time.time(),
-                "data": {
-                    "detections": highest_conf_box if highest_conf_box else None,
-                    "modelId": stream['modelId'],
-                    "modelName": stream['modelName']
-                }
-            }
-            logging.debug(f"Sending detection data for camera {camera_id}: {detection_data}")
-            await websocket.send_json(detection_data)
-            return persistent_data
-        except Exception as e:
-            logging.error(f"Error in handle_detection for camera {camera_id}: {e}")
-            return persistent_data
-
-    def frame_reader(camera_id, cap, buffer, stop_event):
-        import time
-        retries = 0
-        try:
-            while not stop_event.is_set():
-                try:
-                    ret, frame = cap.read()
-                    if not ret:
-                        logging.warning(f"Connection lost for camera: {camera_id}, retry {retries+1}/{max_retries}")
-                        cap.release()
-                        time.sleep(reconnect_interval)
-                        retries += 1
-                        if retries > max_retries and max_retries != -1:
-                            logging.error(f"Max retries reached for camera: {camera_id}")
-                            break
-                        # Re-open the VideoCapture
-                        cap = cv2.VideoCapture(streams[camera_id]['rtsp_url'])
-                        if not cap.isOpened():
-                            logging.error(f"Failed to reopen RTSP stream for camera: {camera_id}")
-                            continue
-                        continue
-                    retries = 0  # Reset on success
-                    if not buffer.empty():
-                        try:
-                            buffer.get_nowait()  # Discard the old frame
-                        except queue.Empty:
-                            pass
-                    buffer.put(frame)
-                except cv2.error as e:
-                    logging.error(f"OpenCV error for camera {camera_id}: {e}")
-                    cap.release()
-                    time.sleep(reconnect_interval)
-                    retries += 1
-                    if retries > max_retries and max_retries != -1:
-                        logging.error(f"Max retries reached after OpenCV error for camera: {camera_id}")
-                        break
-                    # Re-open the VideoCapture
-                    cap = cv2.VideoCapture(streams[camera_id]['rtsp_url'])
-                    if not cap.isOpened():
-                        logging.error(f"Failed to reopen RTSP stream for camera {camera_id} after OpenCV error")
-                        continue
-                except Exception as e:
-                    logging.error(f"Unexpected error for camera {camera_id}: {e}")
-                    cap.release()
-                    break
-        except Exception as e:
-            logging.error(f"Error in frame_reader thread for camera {camera_id}: {e}")
-
-    async def process_streams():
-        global models
-        logging.info("Started processing streams")
-        persistent_data_dict = {} 
-        try:
-            while True:
-                start_time = time.time()
-                # Round-robin processing
-                with streams_lock:
-                    current_streams = list(streams.items())
-                for camera_id, stream in current_streams:
-                    buffer = stream['buffer']
-                    if not buffer.empty():
-                        frame = buffer.get()
-                        with models_lock:
-                            model = models.get(camera_id, {}).get(stream['modelId'])
-                        key = (camera_id, stream['modelId'])
-                        persistent_data = persistent_data_dict.get(key, {})
-                        updated_persistent_data = await handle_detection(camera_id, stream, frame, websocket, model, persistent_data)
-                        persistent_data_dict[key] = updated_persistent_data
-                elapsed_time = (time.time() - start_time) * 1000  # in ms
-                sleep_time = max(poll_interval - elapsed_time, 0)
-                logging.debug(f"Elapsed time: {elapsed_time}ms, sleeping for: {sleep_time}ms")
-                await asyncio.sleep(sleep_time / 1000.0)
-        except asyncio.CancelledError:
-            logging.info("Stream processing task cancelled")
-        except Exception as e:
-            logging.error(f"Error in process_streams: {e}")
-
-    async def send_heartbeat():
-        while True:
-            try:
-                cpu_usage = psutil.cpu_percent()
-                memory_usage = psutil.virtual_memory().percent
-                if torch.cuda.is_available():
-                    gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2)  # Convert to MB
-                    gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)  # Convert to MB
-                else:
-                    gpu_usage = None
-                    gpu_memory_usage = None
-            
-                camera_connections = [
-                    {
-                        "cameraIdentifier": camera_id,
-                        "modelId": stream['modelId'],
-                        "modelName": stream['modelName'],
-                        "online": True
-                    }
-                    for camera_id, stream in streams.items()
-                ]
-            
-                state_report = {
-                    "type": "stateReport",
-                    "cpuUsage": cpu_usage,
-                    "memoryUsage": memory_usage,
-                    "gpuUsage": gpu_usage,
-                    "gpuMemoryUsage": gpu_memory_usage,
-                    "cameraConnections": camera_connections
-                }
-                await websocket.send_text(json.dumps(state_report))
-                logging.debug("Sent stateReport as heartbeat")
-                await asyncio.sleep(HEARTBEAT_INTERVAL)
-            except Exception as e:
-                logging.error(f"Error sending stateReport heartbeat: {e}")
-                break
-
-    async def on_message():
-        global models
-        while True:
-            try:
-                msg = await websocket.receive_text()
-                logging.debug(f"Received message: {msg}")
-                print(f"Received message: {msg}")
-                data = json.loads(msg)
-                msg_type = data.get("type")
-
-                if msg_type == "subscribe":
-                    payload = data.get("payload", {})
-                    camera_id = payload.get("cameraIdentifier")
-                    rtsp_url = payload.get("rtspUrl")
-                    model_url = payload.get("modelUrl")
-                    modelId = payload.get("modelId")
-                    modelName = payload.get("modelName")
-    
-                    if model_url:
-                        with models_lock:
-                            if camera_id not in models:
-                                models[camera_id] = {}
-                            if modelId not in models[camera_id]:
-                                print(f"Downloading model from {model_url}")
-                                parsed_url = urlparse(model_url)
-                                filename = os.path.basename(parsed_url.path)    
-                                model_filename = os.path.join("models", filename)
-                                # Download the model
-                                response = requests.get(model_url, stream=True)
-                                if response.status_code == 200:
-                                    with open(model_filename, 'wb') as f:
-                                        for chunk in response.iter_content(chunk_size=8192):
-                                            f.write(chunk)
-                                    logging.info(f"Downloaded model from {model_url} to {model_filename}")
-                                    model = YOLO(model_filename)
-                                    if torch.cuda.is_available():
-                                        model.to('cuda')
-                                    models[camera_id][modelId] = model
-                                    logging.info(f"Loaded model {modelId} for camera {camera_id}")
-                                else:
-                                    logging.error(f"Failed to download model from {model_url}")
-                                    continue
-                    if camera_id and rtsp_url:
-                        with streams_lock:
-                            if camera_id not in streams and len(streams) < max_streams:
-                                cap = cv2.VideoCapture(rtsp_url)
-                                if not cap.isOpened():
-                                    logging.error(f"Failed to open RTSP stream for camera {camera_id}")
-                                    continue
-                                buffer = queue.Queue(maxsize=1)
-                                stop_event = threading.Event()
-                                thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
-                                thread.daemon = True
-                                thread.start()
-                                streams[camera_id] = {
-                                    'cap': cap,
-                                    'buffer': buffer,
-                                    'thread': thread,
-                                    'rtsp_url': rtsp_url,
-                                    'stop_event': stop_event,
-                                    'modelId': modelId,
-                                    'modelName': modelName
-                                }
-                                logging.info(f"Subscribed to camera {camera_id} with modelId {modelId}, modelName {modelName} and URL {rtsp_url}")
-                            elif camera_id and camera_id in streams:
-                                stream = streams.pop(camera_id)
-                                stream['cap'].release()
-                                logging.info(f"Unsubscribed from camera {camera_id}")
-                                if camera_id in models and modelId in models[camera_id]:
-                                    del models[camera_id][modelId]
-                                    if not models[camera_id]:
-                                        del models[camera_id]
-                elif msg_type == "unsubscribe":
-                    payload = data.get("payload", {})
-                    camera_id = payload.get("cameraIdentifier")
-                    logging.debug(f"Unsubscribing from camera {camera_id}")
-                    with streams_lock:
-                        if camera_id and camera_id in streams:
-                            stream = streams.pop(camera_id)
-                            stream['stop_event'].set()
-                            stream['thread'].join()
-                            stream['cap'].release()
-                            logging.info(f"Unsubscribed from camera {camera_id}")
-                            if camera_id in models and modelId in models[camera_id]:
-                                del models[camera_id][modelId]
-                                if not models[camera_id]:
-                                    del models[camera_id]
-                elif msg_type == "requestState":
-                    # Handle state request
-                    cpu_usage = psutil.cpu_percent()
-                    memory_usage = psutil.virtual_memory().percent
-                    if torch.cuda.is_available():
-                        gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2)  # Convert to MB
-                        gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)  # Convert to MB
-                    else:
-                        gpu_usage = None
-                        gpu_memory_usage = None
-                    
-                    camera_connections = [
-                        {
-                            "cameraIdentifier": camera_id,
-                            "modelId": stream['modelId'],
-                            "modelName": stream['modelName'],
-                            "online": True
-                        }
-                        for camera_id, stream in streams.items()
-                    ]
-                    
-                    state_report = {
-                        "type": "stateReport",
-                        "cpuUsage": cpu_usage,
-                        "memoryUsage": memory_usage,
-                        "gpuUsage": gpu_usage,
-                        "gpuMemoryUsage": gpu_memory_usage,
-                        "cameraConnections": camera_connections
-                    }
-                    await websocket.send_text(json.dumps(state_report))
-                else:
-                    logging.error(f"Unknown message type: {msg_type}")
-            except json.JSONDecodeError:
-                logging.error("Received invalid JSON message")
-            except (WebSocketDisconnect, ConnectionClosedError) as e:
-                logging.warning(f"WebSocket disconnected: {e}")
-                break 
-            except Exception as e:
-                logging.error(f"Error handling message: {e}")
-                break
-
-    try:
-        await websocket.accept()
-        task = asyncio.create_task(process_streams())
-        heartbeat_task = asyncio.create_task(send_heartbeat())
-        message_task = asyncio.create_task(on_message())
-
-        await asyncio.gather(heartbeat_task, message_task)
-    except Exception as e:
-        logging.error(f"Error in detect websocket: {e}")
-    finally:
-        task.cancel()
-        await task
-        with streams_lock:
-            for camera_id, stream in streams.items():
-                stream['stop_event'].set()
-                stream['thread'].join()
-                stream['cap'].release()
-                stream['buffer'].queue.clear()
-                logging.info(f"Released camera {camera_id} and cleaned up resources")
-            streams.clear()
-        with models_lock:
-            models.clear()
-        logging.info("WebSocket connection closed")

pympta.md

@@ -0,0 +1,204 @@
+# pympta: Modular Pipeline Task Executor
+
+`pympta` is a Python module designed to load and execute modular, multi-stage AI pipelines defined in a special package format (`.mpta`). It is primarily used within the detector worker to run complex computer vision tasks where the output of one model can trigger a subsequent model on a specific region of interest.
+
+## Core Concepts
+
+### 1. MPTA Package (`.mpta`)
+
+An `.mpta` file is a standard `.zip` archive with a different extension. It bundles all the necessary components for a pipeline to run.
+
+A typical `.mpta` file has the following structure:
+
+```
+my_pipeline.mpta/
+├── pipeline.json
+├── model1.pt
+├── model2.pt
+└── ...
+```
+
+- **`pipeline.json`**: (Required) The manifest file that defines the structure of the pipeline, the models to use, and the logic connecting them.
+- **Model Files (`.pt`, etc.)**: The actual pre-trained model files (e.g., PyTorch, ONNX). The pipeline currently uses `ultralytics.YOLO` models.
+
+### 2. Pipeline Structure
+
+A pipeline is a tree-like structure of "nodes," defined in `pipeline.json`.
+
+- **Root Node**: The entry point of the pipeline. It processes the initial, full-frame image.
+- **Branch Nodes**: Child nodes that are triggered by specific detection results from their parent. For example, a root node might detect a "vehicle," which then triggers a branch node to detect a "license plate" within the vehicle's bounding box.
+
+This modular structure allows for creating complex and efficient inference logic, avoiding the need to run every model on every frame.
+
+## `pipeline.json` Specification
+
+This file defines the entire pipeline logic. The root object contains a `pipeline` key for the pipeline definition and an optional `redis` key for Redis configuration.
+
+### Top-Level Object Structure
+
+| Key        | Type   | Required | Description                                             |
+| ---------- | ------ | -------- | ------------------------------------------------------- |
+| `pipeline` | Object | Yes      | The root node object of the pipeline.                   |
+| `redis`    | Object | No       | Configuration for connecting to a Redis server.         |
+
+### Redis Configuration (`redis`)
+
+| Key        | Type   | Required | Description                                             |
+| ---------- | ------ | -------- | ------------------------------------------------------- |
+| `host`     | String | Yes      | The hostname or IP address of the Redis server.         |
+| `port`     | Number | Yes      | The port number of the Redis server.                    |
+| `password` | String | No       | The password for Redis authentication.                  |
+| `db`       | Number | No       | The Redis database number to use. Defaults to `0`.      |
+
+### Node Object Structure
+
+| Key                 | Type          | Required | Description                                                                                                                            |
+| ------------------- | ------------- | -------- | -------------------------------------------------------------------------------------------------------------------------------------- |
+| `modelId`           | String        | Yes      | A unique identifier for this model node (e.g., "vehicle-detector").                                                                    |
+| `modelFile`         | String        | Yes      | The path to the model file within the `.mpta` archive (e.g., "yolov8n.pt").                                                            |
+| `minConfidence`     | Float         | Yes      | The minimum confidence score (0.0 to 1.0) required for a detection to be considered valid and potentially trigger a branch.              |
+| `triggerClasses`    | Array<String> | Yes      | A list of class names that, when detected by the parent, can trigger this node. For the root node, this lists all classes of interest. |
+| `crop`              | Boolean       | No       | If `true`, the image is cropped to the parent's detection bounding box before being passed to this node's model. Defaults to `false`.    |
+| `branches`          | Array<Node>   | No       | A list of child node objects that can be triggered by this node's detections.                                                          |
+| `actions`           | Array<Action> | No       | A list of actions to execute upon a successful detection in this node.                                                                 |
+
+### Action Object Structure
+
+Actions allow the pipeline to interact with Redis. They are executed sequentially for a given detection.
+
+#### Action Context & Dynamic Keys
+
+All actions have access to a dynamic context for formatting keys and messages. The context is created for each detection event and includes:
+
+- All key-value pairs from the detection result (e.g., `class`, `confidence`, `id`).
+- `{timestamp_ms}`: The current Unix timestamp in milliseconds.
+- `{uuid}`: A unique identifier (UUID4) for the detection event.
+- `{image_key}`: If a `redis_save_image` action has already been executed for this event, this placeholder will be replaced with the key where the image was stored.
+
+#### `redis_save_image`
+
+Saves the current image frame (or cropped sub-image) to a Redis key.
+
+| Key              | Type   | Required | Description                                                                                             |
+| ---------------- | ------ | -------- | ------------------------------------------------------------------------------------------------------- |
+| `type`           | String | Yes      | Must be `"redis_save_image"`.                                                                           |
+| `key`            | String | Yes      | The Redis key to save the image to. Can contain any of the dynamic placeholders.                        |
+| `expire_seconds` | Number | No       | If provided, sets an expiration time (in seconds) for the Redis key.                                    |
+
+#### `redis_publish`
+
+Publishes a message to a Redis channel.
+
+| Key       | Type   | Required | Description                                                                                             |
+| --------- | ------ | -------- | ------------------------------------------------------------------------------------------------------- |
+| `type`    | String | Yes      | Must be `"redis_publish"`.                                                                              |
+| `channel` | String | Yes      | The Redis channel to publish the message to.                                                            |
+| `message` | String | Yes      | The message to publish. Can contain any of the dynamic placeholders, including `{image_key}`.           |
+
+### Example `pipeline.json` with Redis
+
+This example demonstrates a pipeline that detects vehicles, saves a uniquely named image of each detection that expires in one hour, and then publishes a notification with the image key.
+
+```json
+{
+  "redis": {
+    "host": "redis.local",
+    "port": 6379,
+    "password": "your-super-secret-password"
+  },
+  "pipeline": {
+    "modelId": "vehicle-detector",
+    "modelFile": "vehicle_model.pt",
+    "minConfidence": 0.6,
+    "triggerClasses": ["car", "truck"],
+    "actions": [
+      {
+        "type": "redis_save_image",
+        "key": "detections:{class}:{timestamp_ms}:{uuid}",
+        "expire_seconds": 3600
+      },
+      {
+        "type": "redis_publish",
+        "channel": "vehicle_events",
+        "message": "{\"event\":\"new_detection\",\"class\":\"{class}\",\"confidence\":{confidence},\"image_key\":\"{image_key}\"}"
+      }
+    ],
+    "branches": []
+  }
+}
+```
+
+## API Reference
+
+The `pympta` module exposes two main functions.
+
+### `load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict`
+
+Loads, extracts, and parses an `.mpta` file to build a pipeline tree in memory. It also establishes a Redis connection if configured in `pipeline.json`.
+
+- **Parameters:**
+  - `zip_source` (str): The file path to the local `.mpta` zip archive.
+  - `target_dir` (str): A directory path where the archive's contents will be extracted.
+- **Returns:**
+  - A dictionary representing the root node of the pipeline, ready to be used with `run_pipeline`. Returns `None` if loading fails.
+
+### `run_pipeline(frame, node: dict, return_bbox: bool = False)`
+
+Executes the inference pipeline on a single image frame.
+
+- **Parameters:**
+  - `frame`: The input image frame (e.g., a NumPy array from OpenCV).
+  - `node` (dict): The pipeline node to execute (typically the root node returned by `load_pipeline_from_zip`).
+  - `return_bbox` (bool): If `True`, the function returns a tuple `(detection, bounding_box)`. Otherwise, it returns only the `detection`.
+- **Returns:**
+  - The final detection result from the last executed node in the chain. A detection is a dictionary like `{'class': 'car', 'confidence': 0.95, 'id': 1}`. If no detection meets the criteria, it returns `None` (or `(None, None)` if `return_bbox` is `True`).
+
+## Usage Example
+
+This snippet, inspired by `pipeline_webcam.py`, shows how to use `pympta` to load a pipeline and process an image from a webcam.
+
+```python
+import cv2
+from siwatsystem.pympta import load_pipeline_from_zip, run_pipeline
+
+# 1. Define paths
+MPTA_FILE = "path/to/your/pipeline.mpta"
+CACHE_DIR = ".mptacache"
+
+# 2. Load the pipeline from the .mpta file
+# This reads pipeline.json and loads the YOLO models into memory.
+model_tree = load_pipeline_from_zip(MPTA_FILE, CACHE_DIR)
+
+if not model_tree:
+    print("Failed to load pipeline.")
+    exit()
+
+# 3. Open a video source
+cap = cv2.VideoCapture(0)
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    # 4. Run the pipeline on the current frame
+    # The function will handle the entire logic tree (e.g., find a car, then find its license plate).
+    detection_result, bounding_box = run_pipeline(frame, model_tree, return_bbox=True)
+
+    # 5. Display the results
+    if detection_result:
+        print(f"Detected: {detection_result['class']} with confidence {detection_result['confidence']:.2f}")
+        if bounding_box:
+            x1, y1, x2, y2 = bounding_box
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(frame, detection_result['class'], (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
+
+    cv2.imshow("Pipeline Output", frame)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()
+```

requirements.txt

@@ -5,4 +5,5 @@ torchvision
 ultralytics
 opencv-python
 websockets
-fastapi[standard]
+fastapi[standard]
+redis

siwatsystem/pympta.py

@@ -6,33 +6,60 @@ import cv2
 import requests
 import zipfile
 import shutil
+import traceback
+import redis
+import time
+import uuid
 from ultralytics import YOLO
 from urllib.parse import urlparse
 
-def load_pipeline_node(node_config: dict, mpta_dir: str) -> dict:
+# Create a logger specifically for this module
+logger = logging.getLogger("detector_worker.pympta")
+
+def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client) -> dict:
     # Recursively load a model node from configuration.
     model_path = os.path.join(mpta_dir, node_config["modelFile"])
     if not os.path.exists(model_path):
-        logging.error(f"Model file {model_path} not found.")
+        logger.error(f"Model file {model_path} not found. Current directory: {os.getcwd()}")
+        logger.error(f"Directory content: {os.listdir(os.path.dirname(model_path))}")
         raise FileNotFoundError(f"Model file {model_path} not found.")
-    logging.info(f"Loading model for node {node_config['modelId']} from {model_path}")
+    logger.info(f"Loading model for node {node_config['modelId']} from {model_path}")
     model = YOLO(model_path)
     if torch.cuda.is_available():
+        logger.info(f"CUDA available. Moving model {node_config['modelId']} to GPU")
         model.to("cuda")
+    else:
+        logger.info(f"CUDA not available. Using CPU for model {node_config['modelId']}")
+
+    # Prepare trigger class indices for optimization
+    trigger_classes = node_config.get("triggerClasses", [])
+    trigger_class_indices = None
+    if trigger_classes and hasattr(model, "names"):
+        # Convert class names to indices for the model
+        trigger_class_indices = [i for i, name in model.names.items() 
+                                if name in trigger_classes]
+        logger.debug(f"Converted trigger classes to indices: {trigger_class_indices}")
+
     node = {
         "modelId": node_config["modelId"],
         "modelFile": node_config["modelFile"],
-        "triggerClasses": node_config.get("triggerClasses", []),
+        "triggerClasses": trigger_classes,
+        "triggerClassIndices": trigger_class_indices,
         "crop": node_config.get("crop", False),
         "minConfidence": node_config.get("minConfidence", None),
+        "actions": node_config.get("actions", []),
         "model": model,
-        "branches": []
+        "branches": [],
+        "redis_client": redis_client
     }
+    logger.debug(f"Configured node {node_config['modelId']} with trigger classes: {node['triggerClasses']}")
     for child in node_config.get("branches", []):
-        node["branches"].append(load_pipeline_node(child, mpta_dir))
+        logger.debug(f"Loading branch for parent node {node_config['modelId']}")
+        node["branches"].append(load_pipeline_node(child, mpta_dir, redis_client))
     return node
 
 def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
+    logger.info(f"Attempting to load pipeline from {zip_source} to {target_dir}")
     os.makedirs(target_dir, exist_ok=True)
     zip_path = os.path.join(target_dir, "pipeline.mpta")
     
@@ -40,135 +67,258 @@ def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
     parsed = urlparse(zip_source)
     if parsed.scheme in ("", "file"):
         local_path = parsed.path if parsed.scheme == "file" else zip_source
+        logger.debug(f"Checking if local file exists: {local_path}")
         if os.path.exists(local_path):
             try:
                 shutil.copy(local_path, zip_path)
-                logging.info(f"Copied local .mpta file from {local_path} to {zip_path}")
+                logger.info(f"Copied local .mpta file from {local_path} to {zip_path}")
             except Exception as e:
-                logging.error(f"Failed to copy local .mpta file from {local_path}: {e}")
+                logger.error(f"Failed to copy local .mpta file from {local_path}: {str(e)}", exc_info=True)
                 return None
         else:
-            logging.error(f"Local file {local_path} does not exist.")
+            logger.error(f"Local file {local_path} does not exist. Current directory: {os.getcwd()}")
+            # List all subdirectories of models directory to help debugging
+            if os.path.exists("models"):
+                logger.error(f"Content of models directory: {os.listdir('models')}")
+                for root, dirs, files in os.walk("models"):
+                    logger.error(f"Directory {root} contains subdirs: {dirs} and files: {files}")
+            else:
+                logger.error("The models directory doesn't exist")
             return None
     else:
-        logging.error("HTTP download functionality has been moved. Use a local file path here.")
+        logger.error(f"HTTP download functionality has been moved. Use a local file path here. Received: {zip_source}")
         return None
 
     try:
+        if not os.path.exists(zip_path):
+            logger.error(f"Zip file not found at expected location: {zip_path}")
+            return None
+            
+        logger.debug(f"Extracting .mpta file from {zip_path} to {target_dir}")
+        # Extract contents and track the directories created
+        extracted_dirs = []
         with zipfile.ZipFile(zip_path, "r") as zip_ref:
+            file_list = zip_ref.namelist()
+            logger.debug(f"Files in .mpta archive: {file_list}")
+            
+            # Extract and track the top-level directories
+            for file_path in file_list:
+                parts = file_path.split('/')
+                if len(parts) > 1:
+                    top_dir = parts[0]
+                    if top_dir and top_dir not in extracted_dirs:
+                        extracted_dirs.append(top_dir)
+            
+            # Now extract the files
             zip_ref.extractall(target_dir)
-        logging.info(f"Extracted .mpta file to {target_dir}")
+            
+        logger.info(f"Successfully extracted .mpta file to {target_dir}")
+        logger.debug(f"Extracted directories: {extracted_dirs}")
+        
+        # Check what was actually created after extraction
+        actual_dirs = [d for d in os.listdir(target_dir) if os.path.isdir(os.path.join(target_dir, d))]
+        logger.debug(f"Actual directories created: {actual_dirs}")
+    except zipfile.BadZipFile as e:
+        logger.error(f"Bad zip file {zip_path}: {str(e)}", exc_info=True)
+        return None
     except Exception as e:
-        logging.error(f"Failed to extract .mpta file: {e}")
+        logger.error(f"Failed to extract .mpta file {zip_path}: {str(e)}", exc_info=True)
         return None
     finally:
         if os.path.exists(zip_path):
             os.remove(zip_path)
+            logger.debug(f"Removed temporary zip file: {zip_path}")
+
+    # Use the first extracted directory if it exists, otherwise use the expected name
     pipeline_name = os.path.basename(zip_source)
     pipeline_name = os.path.splitext(pipeline_name)[0]
-    mpta_dir = os.path.join(target_dir, pipeline_name)
+    
+    # Find the directory with pipeline.json
+    mpta_dir = None
+    # First try the expected directory name
+    expected_dir = os.path.join(target_dir, pipeline_name)
+    if os.path.exists(expected_dir) and os.path.exists(os.path.join(expected_dir, "pipeline.json")):
+        mpta_dir = expected_dir
+        logger.debug(f"Found pipeline.json in the expected directory: {mpta_dir}")
+    else:
+        # Look through all subdirectories for pipeline.json
+        for subdir in actual_dirs:
+            potential_dir = os.path.join(target_dir, subdir)
+            if os.path.exists(os.path.join(potential_dir, "pipeline.json")):
+                mpta_dir = potential_dir
+                logger.info(f"Found pipeline.json in directory: {mpta_dir} (different from expected: {expected_dir})")
+                break
+    
+    if not mpta_dir:
+        logger.error(f"Could not find pipeline.json in any extracted directory. Directory content: {os.listdir(target_dir)}")
+        return None
+        
     pipeline_json_path = os.path.join(mpta_dir, "pipeline.json")
     if not os.path.exists(pipeline_json_path):
-        logging.error("pipeline.json not found in the .mpta file")
+        logger.error(f"pipeline.json not found in the .mpta file. Files in directory: {os.listdir(mpta_dir)}")
         return None
 
     try:
         with open(pipeline_json_path, "r") as f:
             pipeline_config = json.load(f)
-        return load_pipeline_node(pipeline_config["pipeline"], mpta_dir)
+        logger.info(f"Successfully loaded pipeline configuration from {pipeline_json_path}")
+        logger.debug(f"Pipeline config: {json.dumps(pipeline_config, indent=2)}")
+        
+        # Establish Redis connection if configured
+        redis_client = None
+        if "redis" in pipeline_config:
+            redis_config = pipeline_config["redis"]
+            try:
+                redis_client = redis.Redis(
+                    host=redis_config["host"],
+                    port=redis_config["port"],
+                    password=redis_config.get("password"),
+                    db=redis_config.get("db", 0),
+                    decode_responses=True
+                )
+                redis_client.ping()
+                logger.info(f"Successfully connected to Redis at {redis_config['host']}:{redis_config['port']}")
+            except redis.exceptions.ConnectionError as e:
+                logger.error(f"Failed to connect to Redis: {e}")
+                redis_client = None
+        
+        return load_pipeline_node(pipeline_config["pipeline"], mpta_dir, redis_client)
+    except json.JSONDecodeError as e:
+        logger.error(f"Error parsing pipeline.json: {str(e)}", exc_info=True)
+        return None
+    except KeyError as e:
+        logger.error(f"Missing key in pipeline.json: {str(e)}", exc_info=True)
+        return None
     except Exception as e:
-        logging.error(f"Error loading pipeline.json: {e}")
+        logger.error(f"Error loading pipeline.json: {str(e)}", exc_info=True)
         return None
 
-def run_pipeline(frame, node: dict, return_bbox: bool = False):
+def execute_actions(node, frame, detection_result):
+    if not node["redis_client"] or not node["actions"]:
+        return
+
+    # Create a dynamic context for this detection event
+    action_context = {
+        **detection_result,
+        "timestamp_ms": int(time.time() * 1000),
+        "uuid": str(uuid.uuid4()),
+    }
+
+    for action in node["actions"]:
+        try:
+            if action["type"] == "redis_save_image":
+                key = action["key"].format(**action_context)
+                _, buffer = cv2.imencode('.jpg', frame)
+                expire_seconds = action.get("expire_seconds")
+                if expire_seconds:
+                    node["redis_client"].setex(key, expire_seconds, buffer.tobytes())
+                    logger.info(f"Saved image to Redis with key: {key} (expires in {expire_seconds}s)")
+                else:
+                    node["redis_client"].set(key, buffer.tobytes())
+                    logger.info(f"Saved image to Redis with key: {key}")
+                # Add the generated key to the context for subsequent actions
+                action_context["image_key"] = key
+            elif action["type"] == "redis_publish":
+                channel = action["channel"]
+                message = action["message"].format(**action_context)
+                node["redis_client"].publish(channel, message)
+                logger.info(f"Published message to Redis channel '{channel}': {message}")
+        except Exception as e:
+            logger.error(f"Error executing action {action['type']}: {e}")
+
+def run_pipeline(frame, node: dict, return_bbox: bool=False):
     """
-    Processes the frame with the given pipeline node. When return_bbox is True,
-    the function returns a tuple (detection, bbox) where bbox is (x1,y1,x2,y2)
-    for drawing. Otherwise, returns only the detection.
+    - For detection nodes (task != 'classify'):
+        • runs `track(..., classes=triggerClassIndices)`
+        • picks top box ≥ minConfidence
+        • optionally crops & resizes → recurse into child
+        • else returns (det_dict, bbox)
+    - For classify nodes:
+        • runs `predict()`
+        • returns top (class,confidence) and no bbox
     """
     try:
-        # Check model type and use appropriate method
-        model_task = getattr(node["model"], "task", None)
-        
-        if model_task == "classify":
-            # Classification models need to use predict() instead of track()
-            logging.debug(f"Running classification model: {node.get('modelId')}")
+        task = getattr(node["model"], "task", None)
+
+        # ─── Classification stage ───────────────────────────────────
+        if task == "classify":
+            # run the classifier and grab its top-1 directly via the Probs API
             results = node["model"].predict(frame, stream=False)
-            detection = None
-            best_box = None
-            
-            # Process classification results
-            for r in results:
-                probs = r.probs
-                if probs is not None and len(probs) > 0:
-                    # Get the most confident class
-                    class_id = int(probs.top1)
-                    conf = float(probs.top1conf)
-                    detection = {
-                        "class": node["model"].names[class_id],
-                        "confidence": conf,
-                        "id": None  # Classification doesn't have tracking IDs
-                    }
-            
-            # Classification doesn't produce bounding boxes
-            bbox = None
-            
-        else:
-            # Detection/segmentation models use tracking
-            logging.debug(f"Running detection/tracking model: {node.get('modelId')}")
-            results = node["model"].track(frame, stream=False, persist=True)
-            detection = None
-            best_box = None
-            max_conf = -1
+            # nothing returned?
+            if not results:
+                return (None, None) if return_bbox else None
 
-            for r in results:
-                for box in r.boxes:
-                    box_cpu = box.cpu()
-                    conf = float(box_cpu.conf[0])
-                    if conf > max_conf and hasattr(box, "id") and box.id is not None:
-                        max_conf = conf
-                        detection = {
-                            "class": node["model"].names[int(box_cpu.cls[0])],
-                            "confidence": conf,
-                            "id": box.id.item()
-                        }
-                        best_box = box_cpu
+            # take the first result's probs object
+            r     = results[0]
+            probs = r.probs
+            if probs is None:
+                return (None, None) if return_bbox else None
 
-            bbox = None
-            # Calculate bbox if best_box exists
-            if detection and best_box is not None:
-                coords = best_box.xyxy[0]
-                x1, y1, x2, y2 = map(int, coords)
-                h, w = frame.shape[:2]
-                x1, y1 = max(0, x1), max(0, y1)
-                x2, y2 = min(w, x2), min(h, y2)
-                if x2 > x1 and y2 > y1:
-                    bbox = (x1, y1, x2, y2)
-                    if node.get("crop", False):
-                        frame = frame[y1:y2, x1:x2]
+            # get the top-1 class index and its confidence
+            top1_idx  = int(probs.top1)
+            top1_conf = float(probs.top1conf)
+
+            det = {
+                "class": node["model"].names[top1_idx],
+                "confidence": top1_conf,
+                "id": None
+            }
+            execute_actions(node, frame, det)
+            return (det, None) if return_bbox else det
+
+
+        # ─── Detection stage ────────────────────────────────────────
+        # only look for your triggerClasses
+        tk = node["triggerClassIndices"]
+        res = node["model"].track(
+            frame,
+            stream=False,
+            persist=True,
+            **({"classes": tk} if tk else {})
+        )[0]
+
+        dets, boxes = [], []
+        for box in res.boxes:
+            conf = float(box.cpu().conf[0])
+            cid  = int(box.cpu().cls[0])
+            name = node["model"].names[cid]
+            if conf < node["minConfidence"]:
+                continue
+            xy = box.cpu().xyxy[0]
+            x1,y1,x2,y2 = map(int, xy)
+            dets.append({"class": name, "confidence": conf,
+                         "id": box.id.item() if hasattr(box, "id") else None})
+            boxes.append((x1, y1, x2, y2))
+
+        if not dets:
+            return (None, None) if return_bbox else None
+
+        # take highest‐confidence
+        best_idx = max(range(len(dets)), key=lambda i: dets[i]["confidence"])
+        best_det = dets[best_idx]
+        best_box = boxes[best_idx]
+
+        # ─── Branch (classification) ───────────────────────────────
+        for br in node["branches"]:
+            if (best_det["class"] in br["triggerClasses"]
+                    and best_det["confidence"] >= br["minConfidence"]):
+                # crop if requested
+                sub = frame
+                if br["crop"]:
+                    x1,y1,x2,y2 = best_box
+                    sub = frame[y1:y2, x1:x2]
+                    sub = cv2.resize(sub, (224, 224))
+
+                det2, _ = run_pipeline(sub, br, return_bbox=True)
+                if det2:
+                    # return classification result + original bbox
+                    execute_actions(br, sub, det2)
+                    return (det2, best_box) if return_bbox else det2
+
+        # ─── No branch matched → return this detection ─────────────
+        execute_actions(node, frame, best_det)
+        return (best_det, best_box) if return_bbox else best_det
 
-        if detection is not None:
-            for branch in node["branches"]:
-                if detection["class"] in branch.get("triggerClasses", []):
-                    min_conf = branch.get("minConfidence")
-                    if min_conf is not None and detection["confidence"] < min_conf:
-                        logging.debug(f"Confidence {detection['confidence']} below threshold {min_conf} for branch {branch['modelId']}.")
-                        if return_bbox:
-                            return detection, bbox
-                        return detection
-                    res = run_pipeline(frame, branch, return_bbox)
-                    if res is not None:
-                        if return_bbox:
-                            return res
-                        return res
-            if return_bbox:
-                return detection, bbox
-            return detection
-        if return_bbox:
-            return None, None
-        return None
     except Exception as e:
-        logging.error(f"Error running pipeline on node {node.get('modelId')}: {e}")
-        if return_bbox:
-            return None, None
-        return None
+        logging.error(f"Error in node {node.get('modelId')}: {e}")
+        return (None, None) if return_bbox else None

test_protocol.py

@@ -0,0 +1,125 @@
+#!/usr/bin/env python3
+"""
+Test script to verify the worker implementation follows the protocol
+"""
+import json
+import asyncio
+import websockets
+import time
+
+async def test_protocol():
+    """Test the worker protocol implementation"""
+    uri = "ws://localhost:8000"
+    
+    try:
+        async with websockets.connect(uri) as websocket:
+            print("✓ Connected to worker")
+            
+            # Test 1: Check if we receive heartbeat (stateReport)
+            print("\n1. Testing heartbeat...")
+            try:
+                message = await asyncio.wait_for(websocket.recv(), timeout=5)
+                data = json.loads(message)
+                if data.get("type") == "stateReport":
+                    print("✓ Received stateReport heartbeat")
+                    print(f"  - CPU Usage: {data.get('cpuUsage', 'N/A')}%")
+                    print(f"  - Memory Usage: {data.get('memoryUsage', 'N/A')}%")
+                    print(f"  - Camera Connections: {len(data.get('cameraConnections', []))}")
+                else:
+                    print(f"✗ Expected stateReport, got {data.get('type')}")
+            except asyncio.TimeoutError:
+                print("✗ No heartbeat received within 5 seconds")
+            
+            # Test 2: Request state
+            print("\n2. Testing requestState...")
+            await websocket.send(json.dumps({"type": "requestState"}))
+            try:
+                message = await asyncio.wait_for(websocket.recv(), timeout=5)
+                data = json.loads(message)
+                if data.get("type") == "stateReport":
+                    print("✓ Received stateReport response")
+                else:
+                    print(f"✗ Expected stateReport, got {data.get('type')}")
+            except asyncio.TimeoutError:
+                print("✗ No response to requestState within 5 seconds")
+            
+            # Test 3: Set session ID
+            print("\n3. Testing setSessionId...")
+            session_message = {
+                "type": "setSessionId",
+                "payload": {
+                    "displayIdentifier": "display-001",
+                    "sessionId": 12345
+                }
+            }
+            await websocket.send(json.dumps(session_message))
+            print("✓ Sent setSessionId message")
+            
+            # Test 4: Test patchSession
+            print("\n4. Testing patchSession...")
+            patch_message = {
+                "type": "patchSession",
+                "sessionId": 12345,
+                "data": {
+                    "currentCar": {
+                        "carModel": "Civic",
+                        "carBrand": "Honda"
+                    }
+                }
+            }
+            await websocket.send(json.dumps(patch_message))
+            
+            # Wait for patchSessionResult
+            try:
+                message = await asyncio.wait_for(websocket.recv(), timeout=5)
+                data = json.loads(message)
+                if data.get("type") == "patchSessionResult":
+                    print("✓ Received patchSessionResult")
+                    print(f"  - Success: {data.get('payload', {}).get('success')}")
+                    print(f"  - Message: {data.get('payload', {}).get('message')}")
+                else:
+                    print(f"✗ Expected patchSessionResult, got {data.get('type')}")
+            except asyncio.TimeoutError:
+                print("✗ No patchSessionResult received within 5 seconds")
+            
+            # Test 5: Test subscribe message format (without actual camera)
+            print("\n5. Testing subscribe message format...")
+            subscribe_message = {
+                "type": "subscribe",
+                "payload": {
+                    "subscriptionIdentifier": "display-001;cam-001",
+                    "snapshotUrl": "http://example.com/snapshot.jpg",
+                    "snapshotInterval": 5000,
+                    "modelUrl": "http://example.com/model.mpta",
+                    "modelName": "Test Model",
+                    "modelId": 101,
+                    "cropX1": 100,
+                    "cropY1": 200,
+                    "cropX2": 300,
+                    "cropY2": 400
+                }
+            }
+            await websocket.send(json.dumps(subscribe_message))
+            print("✓ Sent subscribe message (will fail without actual camera/model)")
+            
+            # Listen for a few more messages to catch any errors
+            print("\n6. Listening for additional messages...")
+            for i in range(3):
+                try:
+                    message = await asyncio.wait_for(websocket.recv(), timeout=2)
+                    data = json.loads(message)
+                    msg_type = data.get("type")
+                    print(f"  - Received {msg_type}")
+                    if msg_type == "error":
+                        print(f"    Error: {data.get('error')}")
+                except asyncio.TimeoutError:
+                    break
+            
+            print("\n✓ Protocol test completed successfully!")
+            
+    except Exception as e:
+        print(f"✗ Connection failed: {e}")
+        print("Make sure the worker is running on localhost:8000")
+
+if __name__ == "__main__":
+    asyncio.run(test_protocol())

worker.md

@@ -0,0 +1,483 @@
+# Worker Communication Protocol
+
+This document outlines the WebSocket-based communication protocol between the CMS backend and a detector worker. As a worker developer, your primary responsibility is to implement a WebSocket server that adheres to this protocol.
+
+## 1. Connection
+
+The worker must run a WebSocket server, preferably on port `8000`. The backend system, which is managed by a container orchestration service, will automatically discover and establish a WebSocket connection to your worker.
+
+Upon a successful connection from the backend, you should begin sending `stateReport` messages as heartbeats.
+
+## 2. Communication Overview
+
+Communication is bidirectional and asynchronous. All messages are JSON objects with a `type` field that indicates the message's purpose, and an optional `payload` field containing the data.
+
+- **Worker -> Backend:** You will send messages to the backend to report status, forward detection events, or request changes to session data.
+- **Backend -> Worker:** The backend will send commands to you to manage camera subscriptions.
+
+## 3. Dynamic Configuration via MPTA File
+
+To enable modularity and dynamic configuration, the backend will send you a URL to a `.mpta` file when it issues a `subscribe` command. This file is a renamed `.zip` archive that contains everything your worker needs to perform its task.
+
+**Your worker is responsible for:**
+
+1. Fetching this file from the provided URL.
+2. Extracting its contents.
+3. Interpreting the contents to configure its internal pipeline.
+
+**The contents of the `.mpta` file are entirely up to the user who configures the model in the CMS.** This allows for maximum flexibility. For example, the archive could contain:
+
+- AI/ML Models: Pre-trained models for libraries like TensorFlow, PyTorch, or ONNX.
+- Configuration Files: A `config.json` or `pipeline.yaml` that defines a sequence of operations, specifies model paths, or sets detection thresholds.
+- Scripts: Custom Python scripts for pre-processing or post-processing.
+- API Integration Details: A JSON file with endpoint information and credentials for interacting with third-party detection services.
+
+Essentially, the `.mpta` file is a self-contained package that tells your worker *how* to process the video stream for a given subscription.
+
+## 4. Messages from Worker to Backend
+
+These are the messages your worker is expected to send to the backend.
+
+### 4.1. State Report (Heartbeat)
+
+This message is crucial for the backend to monitor your worker's health and status, including GPU usage.
+
+- **Type:** `stateReport`
+- **When to Send:** Periodically (e.g., every 2 seconds) after a connection is established.
+
+**Payload:**
+
+```json
+{
+  "type": "stateReport",
+  "cpuUsage": 75.5,
+  "memoryUsage": 40.2,
+  "gpuUsage": 60.0,
+  "gpuMemoryUsage": 25.1,
+  "cameraConnections": [
+    {
+      "subscriptionIdentifier": "display-001;cam-001",
+      "modelId": 101,
+      "modelName": "General Object Detection",
+      "online": true,
+      "cropX1": 100,
+      "cropY1": 200,
+      "cropX2": 300,
+      "cropY2": 400
+    }
+  ]
+}
+```
+
+> **Note:**
+>
+> - `cropX1`, `cropY1`, `cropX2`, `cropY2` (optional, integer) should be included in each camera connection to indicate the crop coordinates for that subscription.
+
+### 4.2. Image Detection
+
+Sent when the worker detects a relevant object. The `detection` object should be flat and contain key-value pairs corresponding to the detected attributes.
+
+- **Type:** `imageDetection`
+
+**Payload Example:**
+
+```json
+{
+  "type": "imageDetection",
+  "subscriptionIdentifier": "display-001;cam-001",
+  "timestamp": "2025-07-14T12:34:56.789Z",
+  "data": {
+    "detection": {
+      "carModel": "Civic",
+      "carBrand": "Honda",
+      "carYear": 2023,
+      "bodyType": "Sedan",
+      "licensePlateText": "ABCD1234",
+      "licensePlateConfidence": 0.95
+    },
+    "modelId": 101,
+    "modelName": "US-LPR-and-Vehicle-ID"
+  }
+}
+```
+
+### 4.3. Patch Session
+
+> **Note:** Patch messages are only used when the worker can't keep up and needs to retroactively send detections. Normally, detections should be sent in real-time using `imageDetection` messages. Use `patchSession` only to update session data after the fact.
+
+Allows the worker to request a modification to an active session's data. The `data` payload must be a partial object of the `DisplayPersistentData` structure.
+
+- **Type:** `patchSession`
+
+**Payload Example:**
+
+```json
+{
+  "type": "patchSession",
+  "sessionId": 12345,
+  "data": {
+    "currentCar": {
+        "carModel": "Civic",
+        "carBrand": "Honda",
+        "licensePlateText": "ABCD1234"
+    }
+  }
+}
+```
+
+The backend will respond with a `patchSessionResult` command.
+
+#### `DisplayPersistentData` Structure
+
+The `data` object in the `patchSession` message is merged with the existing `DisplayPersistentData` on the backend. Here is its structure:
+
+```typescript
+interface DisplayPersistentData {
+    progressionStage: "welcome" | "car_fueling" | "car_waitpayment" | "car_postpayment" | null;
+    qrCode: string | null;
+    adsPlayback: {
+        playlistSlotOrder: number; // The 'order' of the current slot
+        adsId: number | null;
+        adsUrl: string | null;
+    } | null;
+    currentCar: {
+        carModel?: string;
+        carBrand?: string;
+        carYear?: number;
+        bodyType?: string;
+        licensePlateText?: string;
+        licensePlateType?: string;
+    } | null;
+    fuelPump: { /* FuelPumpData structure */ } | null;
+    weatherData: { /* WeatherResponse structure */ } | null;
+    sessionId: number | null;
+}
+```
+
+#### Patching Behavior
+
+- The patch is a **deep merge**.
+- **`undefined`** values are ignored.
+- **`null`** values will set the corresponding field to `null`.
+- Nested objects are merged recursively.
+
+## 5. Commands from Backend to Worker
+
+These are the commands your worker will receive from the backend.
+
+### 5.1. Subscribe to Camera
+
+Instructs the worker to process a camera's RTSP stream using the configuration from the specified `.mpta` file.
+
+- **Type:** `subscribe`
+
+**Payload:**
+
+```json
+{
+  "type": "subscribe",
+  "payload": {
+    "subscriptionIdentifier": "display-001;cam-002",
+    "rtspUrl": "rtsp://user:pass@host:port/stream",
+    "snapshotUrl": "http://go2rtc/snapshot/1",
+    "snapshotInterval": 5000,
+    "modelUrl": "http://storage/models/us-lpr.mpta",
+    "modelName": "US-LPR-and-Vehicle-ID",
+    "modelId": 102,
+    "cropX1": 100,
+    "cropY1": 200,
+    "cropX2": 300,
+    "cropY2": 400
+  }
+}
+```
+
+> **Note:**
+>
+> - `cropX1`, `cropY1`, `cropX2`, `cropY2` (optional, integer) specify the crop coordinates for the camera stream. These values are configured per display and passed in the subscription payload. If not provided, the worker should process the full frame.
+>
+> **Important:**
+> If multiple displays are bound to the same camera, your worker must ensure that only **one stream** is opened per camera. When you receive multiple subscriptions for the same camera (with different `subscriptionIdentifier` values), you should:
+>
+> - Open the RTSP stream **once** for that camera if using RTSP.
+> - Capture each snapshot only once per cycle, and reuse it for all display subscriptions sharing that camera.
+> - Capture each frame/image only once per cycle.
+> - Reuse the same captured image and snapshot for all display subscriptions that share the camera, processing and routing detection results separately for each display as needed.
+> This avoids unnecessary load and bandwidth usage, and ensures consistent detection results and snapshots across all displays sharing the same camera.
+
+### 5.2. Unsubscribe from Camera
+
+Instructs the worker to stop processing a camera's stream.
+
+- **Type:** `unsubscribe`
+
+**Payload:**
+
+```json
+{
+  "type": "unsubscribe",
+  "payload": {
+    "subscriptionIdentifier": "display-001;cam-002"
+  }
+}
+```
+
+### 5.3. Request State
+
+Direct request for the worker's current state. Respond with a `stateReport` message.
+
+- **Type:** `requestState`
+
+**Payload:**
+
+```json
+{
+  "type": "requestState"
+}
+```
+
+### 5.4. Patch Session Result
+
+Backend's response to a `patchSession` message.
+
+- **Type:** `patchSessionResult`
+
+**Payload:**
+
+```json
+{
+  "type": "patchSessionResult",
+  "payload": {
+    "sessionId": 12345,
+    "success": true,
+    "message": "Session updated successfully."
+  }
+}
+```
+
+### 5.5. Set Session ID
+
+Allows the backend to instruct the worker to associate a session ID with a subscription. This is useful for linking detection events to a specific session. The session ID can be `null` to indicate no active session.
+
+- **Type:** `setSessionId`
+
+**Payload:**
+
+```json
+{
+  "type": "setSessionId",
+  "payload": {
+    "displayIdentifier": "display-001",
+    "sessionId": 12345
+  }
+}
+```
+
+Or to clear the session:
+
+```json
+{
+  "type": "setSessionId",
+  "payload": {
+    "displayIdentifier": "display-001",
+    "sessionId": null
+  }
+}
+```
+
+> **Note:**
+>
+> - The worker should store the session ID for the given subscription and use it in subsequent detection or patch messages as appropriate. If `sessionId` is `null`, the worker should treat the subscription as having no active session.
+
+## Subscription Identifier Format
+
+The `subscriptionIdentifier` used in all messages is constructed as:
+
+```
+displayIdentifier;cameraIdentifier
+```
+
+This uniquely identifies a camera subscription for a specific display.
+
+### Session ID Association
+
+When the backend sends a `setSessionId` command, it will only provide the `displayIdentifier` (not the full `subscriptionIdentifier`).
+
+**Worker Responsibility:**
+
+- The worker must match the `displayIdentifier` to all active subscriptions for that display (i.e., all `subscriptionIdentifier` values that start with `displayIdentifier;`).
+- The worker should set or clear the session ID for all matching subscriptions.
+
+## 6. Example Communication Log
+
+This section shows a typical sequence of messages between the backend and the worker. Patch messages are not included, as they are only used when the worker cannot keep up.
+
+> **Note:** Unsubscribe is triggered when a user removes a camera or when the node is too heavily loaded and needs rebalancing.
+
+1.  **Connection Established** & **Heartbeat**
+    *   **Worker -> Backend**
+    ```json
+    {
+      "type": "stateReport",
+      "cpuUsage": 70.2,
+      "memoryUsage": 38.1,
+      "gpuUsage": 55.0,
+      "gpuMemoryUsage": 20.0,
+      "cameraConnections": []
+    }
+    ```
+2.  **Backend Subscribes Camera**
+    *   **Backend -> Worker**
+    ```json
+    {
+      "type": "subscribe",
+      "payload": {
+        "subscriptionIdentifier": "display-001;entry-cam-01",
+        "rtspUrl": "rtsp://192.168.1.100/stream1",
+        "modelUrl": "http://storage/models/vehicle-id.mpta",
+        "modelName": "Vehicle Identification",
+        "modelId": 201
+      }
+    }
+    ```
+3.  **Worker Acknowledges in Heartbeat**
+    *   **Worker -> Backend**
+    ```json
+    {
+      "type": "stateReport",
+      "cpuUsage": 72.5,
+      "memoryUsage": 39.0,
+      "gpuUsage": 57.0,
+      "gpuMemoryUsage": 21.0,
+      "cameraConnections": [
+        {
+          "subscriptionIdentifier": "display-001;entry-cam-01",
+          "modelId": 201,
+          "modelName": "Vehicle Identification",
+          "online": true
+        }
+      ]
+    }
+    ```
+4.  **Worker Detects a Car**
+    *   **Worker -> Backend**
+    ```json
+    {
+      "type": "imageDetection",
+      "subscriptionIdentifier": "display-001;entry-cam-01",
+      "timestamp": "2025-07-15T10:00:00.000Z",
+      "data": {
+        "detection": {
+          "carBrand": "Honda",
+          "carModel": "CR-V",
+          "bodyType": "SUV",
+          "licensePlateText": "GEMINI-AI",
+          "licensePlateConfidence": 0.98
+        },
+        "modelId": 201,
+        "modelName": "Vehicle Identification"
+      }
+    }
+    ```
+    *   **Worker -> Backend**
+    ```json
+    {
+      "type": "imageDetection",
+      "subscriptionIdentifier": "display-001;entry-cam-01",
+      "timestamp": "2025-07-15T10:00:01.000Z",
+      "data": {
+        "detection": {
+          "carBrand": "Toyota",
+          "carModel": "Corolla",
+          "bodyType": "Sedan",
+          "licensePlateText": "CMS-1234",
+          "licensePlateConfidence": 0.97
+        },
+        "modelId": 201,
+        "modelName": "Vehicle Identification"
+      }
+    }
+    ```
+    *   **Worker -> Backend**
+    ```json
+    {
+      "type": "imageDetection",
+      "subscriptionIdentifier": "display-001;entry-cam-01",
+      "timestamp": "2025-07-15T10:00:02.000Z",
+      "data": {
+        "detection": {
+          "carBrand": "Ford",
+          "carModel": "Focus",
+          "bodyType": "Hatchback",
+          "licensePlateText": "CMS-5678",
+          "licensePlateConfidence": 0.96
+        },
+        "modelId": 201,
+        "modelName": "Vehicle Identification"
+      }
+    }
+    ```
+5.  **Backend Unsubscribes Camera**
+    *   **Backend -> Worker**
+    ```json
+    {
+      "type": "unsubscribe",
+      "payload": {
+        "subscriptionIdentifier": "display-001;entry-cam-01"
+      }
+    }
+    ```
+6.  **Worker Acknowledges Unsubscription**
+    *   **Worker -> Backend**
+    ```json
+    {
+      "type": "stateReport",
+      "cpuUsage": 68.0,
+      "memoryUsage": 37.0,
+      "gpuUsage": 50.0,
+      "gpuMemoryUsage": 18.0,
+      "cameraConnections": []
+    }
+    ```
+## 7. HTTP API: Image Retrieval
+
+In addition to the WebSocket protocol, the worker exposes an HTTP endpoint for retrieving the latest image frame from a camera.
+
+### Endpoint
+
+```
+GET /camera/{camera_id}/image
+```
+
+- **`camera_id`**: The full `subscriptionIdentifier` (e.g., `display-001;cam-001`).
+
+### Response
+
+- **Success (200):** Returns the latest JPEG image from the camera stream.
+    - `Content-Type: image/jpeg`
+    - Binary JPEG data.
+
+- **Error (404):** If the camera is not found or no frame is available.
+    - JSON error response.
+
+- **Error (500):** Internal server error.
+
+### Example Request
+
+```
+GET /camera/display-001;cam-001/image
+```
+
+### Example Response
+
+- **Headers:**
+    ```
+    Content-Type: image/jpeg
+    ```
+- **Body:** Binary JPEG image.
+
+### Notes
+
+- The endpoint returns the most recent frame available for the specified camera subscription.
+- If multiple displays share the same camera, each subscription has its own buffer; the endpoint uses the buffer for the given `camera_id`.
+- This API is useful for debugging, monitoring, or integrating with external systems that require direct image access.